No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Face Recognition Based on Facial Features
Abstract
Commencing from the last decade several different methods have been planned and developed in the prospect of face recognition that is one of the chief stimulating zone in the area of image processing. Face recognitions processes have various applications in the prospect of security systems and crime investigation systems. The study is basically comprised of three phases, i.e., face detection, facial features extraction and face recognition. The first phase is the face detection process where region of interest i.e., features region is extracted. The 2 nd phase is features extraction. Here face features i.e., eyes, nose and lips are extracted out commencing the extracted face area. The last module is the face recognition phase which makes use of the extracted left eye for the recognition purpose by combining features of Eigenfeatures and Fisherfeatures.
... Human face recognition is widely used to authenticate identities and prevent unauthorized access to organization's physical facilities, networks and database networks due to it's because it is not intrusive nature which creates an opportunity for distant authentication checks without the one's attention [1]. Although there is registered progress in the domain of face detection and recognition for security, there are still existing issues hindering the progress to reach or surpass human level accuracy [2]. ese issues are variations in human facial appearance such as; varying lighting condition, noise in face images, scale, pose, masked face [1] and eliminating racial discrimination in face recognition. ...
... Model performance for the di erent proposed pre-trained models and training from scratch method was evaluated and compared using four performance metrics, including precision, recall, accuracy and F1-socre, as shown in equation (1)(2)(3)(4). ...
... Model performance for the different proposed pre-trained models and training from method was evaluated and compared using four performance metrics, including precisio accuracy and F1-socre, as shown in equation (1)(2)(3)(4). Where; TP is true positive value, TN is true negative value, FP is false positive value, F negative value Where; TP is true positive value, TN is true negative value, FP is false positive value, FN is false negative value. ...
Currently, many institutions of higher learning in Uganda are faced with major security threats ranging from burglary to cyber threats. Consequently, the institutions have recruited and deployed several trained personnel to offer the desired security. As human beings, these personnel can make errors either by commission or omission. To overcome the limitation of trained security personnel, a number of face recognition models that detect masked and unmasked faces automatically for allowing access to sensitive premises have been developed. However, the state-of-the art of these models are not generalizable across populations and probably will not work in the Ugandan context because they have not been implemented with capabilities to eliminate racial discrimination in face recognition. This study therefore developed a deep learning model for masked and unmasked face recognition based on local context. The model was trained and tested on 1000 images taken from students of Kabale University using Nikon d850 camera. Machine learning techniques such as Principal Component Analysis, Geometric Feature Based Methods and double threshold techniques were used in the development phase while results were classified using CNN pre-trained models. From results obtained, VGG19 achieved the higher accuracy of 91.2% followed by Inception V 3 at 90.3% and VGG16 with 89.69% whereas the developed model achieved 90.32%.
... In part-based methods, the face image is divided into several overlapping and/or non-overlapping parts which are then used for recognition [2]. Feature-based methods consider all facial features like eye, nose and mouth region individually [3]. Fractal-based methods computes self-similarities among the image. ...
... It tunes the number of face-component required to be eligible partially occluded face as face-present. If four major face component [Left eye, Right eye, Nose, Mouth] are considered, then threshold T will be in the range [1][2][3][4]. Minimum threshold T is 1, which indicates detection is concluded on single facecomponent and it produce weak face detection. And if T is 4, it indicates that detection is concluded on all face-component, which produces strong face detection. ...
... Face recognition technology [1] is essential research in computer vision and pattern recognition [2][3][4]. This technology determines data consistent visual features [5] of the face image. It is part of criminal investigation, authentication, and video surveillance [6]. ...
Aim
The proposed work aims to monitor real-time attendance using face recognition in every institutional sector. It is one of the key concerns in every organization.
Background
Nowadays, most organizations spend a lot of time marking attendance for a large number of individuals manually. Many technologies like Radio Frequency Identification (RFID), and biometric systems are introduced to overcome the manual attendance system. However, not all of these technologies are automatic, and people must queue to have their presence recorded.
Objective
The main objective of the system is to provide an automated attendance system with the help of face recognition owing to the difficulty in the manual as well as other traditional attendance systems.
Methods
The main objective of the system is to provide an automated attendance system with the help of face recognition owing to the difficulty in the manual as well as other traditional attendance systems.
Results
Using the web camera connected to the computer, face detection and recognition are performed, and recognized faces are attended. Here, the admin module and teacher modules are implemented with different functionalities to monitor attendance.
Conclusion
Experiment results get 94.5% accuracy of face detection and 98.5% accuracy of face recognition by using the Haarcascade classifier and LBPH algorithm. This application system will be simple to implement, accurate, and efficient in monitoring attendance in real-time.
... In emergencies, people who prefer to travel in a bus for long distances will be suffering a lot due to the unavailability of seats. Especially the aged people and the children will be affected as they may not get a chair for their entire journey [3]. Our system helps the people know the vacant seats of buses in advance just five minutes before the bus arrival time. ...
A task to identify occupied seats in government buses for passengers emerges in the digital communicating world. In this project, the second stage is verifying a system with tagged seat sense and a camera for detecting whether the seat is occupied. It sees the availability or occupancy status of seats in any coach. In this work, detection of face overcomes the drawback caused due to seat sensor that indicates the seat is occupied when any objects are placed. The camera is inserted in the buses, connected with Raspberry Pi 3 B+ microcontroller. When the vehicle starts from its corresponding station, the images of people traveling are captured, adjusted, and improved by software communication. The transferred images are processed to count the commuters in the vehicle to find the vacancy of seats so that the passengers waiting in various stops can monitor the seat is available or not.
... Face recognition systems work according to the three sub processes. They are face detection, facial feature extraction and face recognition [3]. Face detection means detect the human face in a given image. ...
Face recognition widely uses in security applications in modern world due to the improvement of machine accuracy of face recognition systems over the last decade. As the time passes, various challenges has been affected on these security applications. Facial cosmetic alteration is one of the challenge that has been affected on the accuracy of face recognition systems. There are several research studies has been done about the impact of facial makeup on face recognition and also about the makeup detection algorithms. This paper describe in detail about the impact of facial makeup on the accuracy of face recognition systems. In order to that here we discuss about how to identify facial features using different facial feature extraction approaches, Face recognition techniques, Makeup and modification on facial features and classifiers in face recognition. This work clearly indicates that facial cosmetic degrade the accuracy of face recognition systems as well as it tends to shows us the importance of makeup detection algorithms to overcome the application of makeup.
... In face recognition approaches, there are many methods that are based on feature extraction of the face to recognize an individual. These features include eye, nose, ears, etc. and their geometric connections [10]. Human brain automatically incorporates these features as soon as it sees an individual. ...
... Wencang Zhao proposed a new image segmentation algorithm based on textural features and Neural Network to separate the targeted images from the background [21,22,23]. Dataset of micro-CT images is used. ...
... If divide that category then there are mainly two types of category the first one is appearance based approach and the other one is geometric based approach. And one more approach is multi view approach in this technique store multi view images in data base and then compare the image from the data base [40][41]. Now a days most of the advancement in 2D grey scale images because it demands the face recognition in real time environment. ...
System that relay on face recognition biometrics have gained great impact on security system since security threats are imposed weakness among the implemented security system. Other biometrics which used for security like fingerprints have some issues and they are not trust worthy. In this survey paper we discussed different types of existing face recognition techniques along with their pros and cons. Face recognition is not a simple thing its very complex system because in face recognition if the user hide their face with sunglasses or hide the face with hijab then its difficult task to recognize the face for that purpose in face recognition used different types of techniques which can recognize the faces in this survey paper we discussed these techniques.
... If divide that category then there are mainly two types of category the first one is appearance based approach and the other one is geometric based approach. And one more approach is multi view approach in this technique store multi view images in data base and then compare the image from the data base [40][41]. Now a days most of the advancement in 2D grey scale images because it demands the face recognition in real time environment. ...
System that relay on face recognition biometrics have gained great impact on security system since security threats are imposed weakness among the implemented security system. Other biometrics which used for security like fingerprints have some issues and they are not trust worthy. In this survey paper we discussed different types of existing face recognition techniques along with their pros and cons. Face recognition is not a simple thing its very complex system because in face recognition if the user hide their face with sunglasses or hide the face with hijab then its difficult task to recognize the face for that purpose in face recognition used different types of techniques which can recognize the faces in this survey paper we discussed these techniques.
... This constraint and imposes a large range of technical challenges for such systems in image processing, analysis, and understanding. In Face Recognition, there are different challenges [1][2][3][4] such as a large set of images, inappropriate illuminating [5][6]. For solving these issues a general statement of the issue can be resolved, formulated and observed first. ...
Face recognition has gained a significant position among most commonly used applications of image processing furthermore availability of viable technologies in this field have contributed a great deal to it. In spite of rapid progress in this field it still has to overcome various challenges like Aging, Partial Occlusion, and Facial Expressions etc affecting the performance of the system, are covered in first part of the survey. This part also highlights the most commonly used databases, available as a standard for face recognition tests. AT & T, AR Database, FERET, ORL and Yale Database have been outlined here. While in the second part of this survey a detailed overview of some important existing methods which are used to dealing the issues of face recognition have been presented. Said methods include Eigenface, Neural Network (NN), Support Vector Machine (SVM), Gabor Wavelet and Hidden Markov Model (HMM). While in last part of the survey several applications of a face recognition system such as video surveillance, Access Control, and Pervasive Computing has been discussed.
... A method called AFMC [48] was proposed which not only eliminated SSS problem but also the results shows that the algorithm is effective, efficient and reduced the computational cost.Further pose variation and SSS problems was taken in account by [49] and proposed a framework, that depict fine performance against pose variation and SSS problem. In another research work [50], researchers have used eye region of the face for recognition, the results were encouraging. ...
Face recognition is a necessity of the modern age as the need for identification of individual has increased with the globalization of the world. Personal authentication through face has been under research since last two decades. The performance of the face recognition system has been enhanced using various algorithms. A generic facial authentication method contains three major steps i.e. face detection, facial features segmentation and face recognition. There are many commonly used algorithms used for this purpose. This paper provides an overview of different face recognition approaches. These approaches are categorized into four classes in this paper. These are holistic based approach, model based approach, hybrid based approach and feature based approach. Various techniques introduced in each of these categories are discussed.
... Wencang Zhao [29] proposed a new image segmentation algorithm based on textural features [30] and Neural Network [31] to separate the targeted images from background. Dataset of micro-CT images are used. ...
Segmentation is considered as one of the main steps in image processing. It divides a digital image into multiple regions in order to analyze them. It is also used to distinguish different objects in the image. Several image segmentation techniques have been developed by the researchers in order to make images smooth and easy to evaluate. This paper presents a literature review of basic image segmentation techniques from last five years. Recent research in each of image segmentation technique is presented in this paper.
... If divide that category then there are mainly two types of category the first one is appearance based approach and the other one is geometric based approach. And one more approach is multi view approach in this technique store multi view images in data base and then compare the image from the data base [40][41]. Now a days most of the advancement in 2D grey scale images because it demands the face recognition in real time environment. ...
System that relay on face recognition biometrics have gained great impact on security system since security threats
are imposed weakness among the implemented security system. Other biometrics which used for security like fingerprints have
some issues and they are not trust worthy. In this survey paper we discussed different types of existing face recognition
techniques along with their pros and cons. Face recognition is not a simple thing its very complex system because in face
recognition if the user hide their face with sunglasses or hide the face with hijab then its difficult task to recognize the face for
that purpose in face recognition used different types of techniques which can recognize the faces in this survey paper we
discussed these techniques.
... For instance we can analyze [10] here that is using nose heuristics for the recognition of face. Another example of such approaches can be analyzed in [11], where facial features like eyes, nose and lips are extracted from the detected face for the recognition purpose. Localization of facial features including eyes, nose and lips using Gabor filters for face recognition is presented in [12]. ...
Face recognition has been in spot light for last few decades by keeping in view its increasing usage in real world applications, still challenges are there to meet, especially in real world applications. A lot of work has been reported on face recognition during the recent decades, some of which have also come up with their modifications. This paper presents a detailed analysis on the importance and application of face recognition technology, mentioning the factors affecting its applicability in real life. In addition, several face recognition techniques along with their experimental results are also discussed. The issues that still need to be addressed are mentioned here as well.
... Biometrics is emerging as a leading technology now days. The most important thing in biometrics is to recognize the features (Sharif et al., 2011) like, Face (Sharif et al., 2012), Finger Prints, Iris and Gait etc. Technological revolution in science and engineering has helped human beings to overcome their weakness and make their existence more valuable. Everyone is born with some abilities along with some disabilities too. ...
The basic motivation behind this research work is to assist enormous number of disable people to enhance their capabilities regardless of their disability. In particular, we have focused and addressed the deafness and dumbness disability in human beings on technological basis. The aim was to design a system to help such people. Sign language has been used as our database in whole recognition process. The gestures have been read by comparison with available signs in the database. The work is comprised of three major parts. 1) Acquiring images in real time environment through any imaging device. 2) Recognition of those images on the basis of probability by comparing with the database. 3) Finally translating recognized images into possible output. We have used various algorithms to validate the approach and to check the efficiency. In particular, mainly adaboost and Support Vector Machine (SVM) algorithms have been tested. Both of these algorithms worked well but SVM was found to be optimum with respect to time efficiency as compared with adaboost.
... Biometrics is emerging as a leading technology now days. The most important thing in biometrics is to recognize the features (Sharif et al., 2011) like, Face (Sharif et al., 2012), Finger Prints, Iris and Gait etc. Technological revolution in science and engineering has helped human beings to overcome their weakness and make their existence more valuable. Everyone is born with some abilities along with some disabilities too. ...
The basic motivation behind this research work is to assist enormous number of disable people to enhance their capabilities regardless of their disability. In particular, we have focused and addressed the deafness and dumbness disability in human beings on technological basis. The aim was to design a system to help such people. Sign language has been used as our database in whole recognition process. The gestures have been read by comparison with available signs in the database. The work is comprised of three major parts. 1) Acquiring images in real time environment through any imaging device. 2) Recognition of those images on the basis of probability by comparing with the database. 3) Finally translating recognized images into possible output. We have used various algorithms to validate the approach and to check the efficiency. In particular, mainly adaboost and Support Vector Machine (SVM) algorithms have been tested. Both of these algorithms worked well but SVM was found to be optimum with respect to time efficiency as compared with adaboost.
... 1. Face detection [2] 2. Face alignment 3. Feature detection and extraction [3] 4. Processing on features 5. Face recognition ...
In this paper a new technique of facial feature detection for face recognition is proposed which can also be helpful for pose variation. It is hybrid facial feature detection technique. Key feature of this proposed technique is vertical and horizontal segmentations and then applying the template matching technique. After template matching, the face features are calculated in numerical or in hashed form. At the end the face is searched on the basis of calculated hash values.
... Feature based methods that deal with the problem of occlusion take into consideration the individual features [43], like areas around eye, nose and mouth region and ignore the other features that can be distinctive among various individuals. Probability is considered to whether the image found its correct match [34]. ...
Systems that rely on Face Recognition (FR) biometric have gained great importance ever since terrorist threats imposed weakness among the implemented security systems. Other biometrics i.e., fingerprints or iris recognition is not trustworthy in such situations whereas FR is considered as a fine compromise. This survey illustrates different FR practices that laid foundations on the issue of partial occlusion dilemma where faces are disguised to cheat the security system. Occlusion refers to facade of the face image which can be due to sunglasses, hair or wrapping of facial image by scarf or other accessories. Efforts on FR in controlled settings have been in the picture for past several years; however identification under uncontrolled conditions like illumination, expression and partial occlusion is quite a matter of concern. Based on
literature a classification is made in this paper to solve the recognition of face in the presence of partial occlusion. These methods are named as part based methods that make use of Principal Component Analysis (PCA), Linear Discriminate Analysis (LDA), Non-negative Matrix Factorization (NMF), Local Non-negative Matrix Factorization (LNMF), Independent Component Analysis (ICA) and other variations. Feature based and fractal based methods consider features around eyes, nose or mouth region to be used in the recognition phase of algorithms. Furthermore the paper details the experiments and databases used by an assortment of authors to handle the problem of occlusion and the results obtained after performing
diverse set of analysis. Lastly, a comparison of various techniques is shown in tabular format to give a precise overview of what different authors have already projected in this particular field.
... In some image processing techniques, feature extraction [38] is very complicated in some critical biometrics like finger prints [14] [36] where features are so, minute that cannot be seen with the necked eye. But face is one of the most important corporals biometric that shows reliable results as compare to other biometrics [41]. ...
... In some image processing techniques, feature extraction [38] is very complicated in some critical biometrics like finger prints [14] [36] where features are so, minute that cannot be seen with the necked eye. But face is one of the most important corporals biometric that shows reliable results as compare to other biometrics [41]. ...
Face recognition is considered to be one of the most reliable biometric, when security issues are taken into concern. For this, feature extraction becomes a critical problem. Different methods are used for extraction of facial feature which are broadly classified into linear and nonlinear subspaces. Among the linear methods are Linear Discriminant Analysis (LDA), Bayesian Methods (MAP and ML), Discriminative Common Vectors (DCV), Independent Component Analysis (ICA), Tensor faces Multi-Linear Singular Value Decomposition(SVD), Two Dimensional PCA (2DPCA), Two Dimensional LDA (2D-LDA)etc., but Principal Component Analysis (PCA) is considered to be one the classic method in this field. Based on this a brief
comparison of PCA family is drawn, of which PCA, Kernel PCA (KPCA), 2DPCA and Two Dimensional Kernel (2DKPCA) are of major concern. Based on literature review recognition performance of PCA family is analyzed using the databases named YALE, YALE-B, ORL and CMU. Concluding remarks about testing criteria set by different authors as listed in literature reveals that K series of PCA produced better results as compared to simple PCA and 2DPCA on the aforementioned datasets
... It is very difficult for a human eye to detect them in X-Rays. In this paper microscopic feature extraction [12][13][14] technique to detect the minute holes in bones [1] is described in order to find out the clear view of desired area of image. ...
In this paper a new method of microscopic feature extraction on image processing has been proposed. The
proposed technique is effective in extracting desired microscopic features from an image. In this technique
dynamic threshold technique is applied on the image in order to remove the background, then vector median
filter is applied to remove the noisy pixels for achieving clear image, and finally by digital morphological algorithm to find the desired location in an image is obtained.
... Feature based methods that deal with the problem of occlusion take into consideration the individual features [43] like areas around eye, nose and mouth region and ignore the other features that can be distinctive among various individuals. Probability is considered to whether the image found its correct match [34]. ...
Systems that rely on Face Recognition (FR) biometric have gained great importance ever since terrorist threats imposed weakness among the implemented security systems. Other biometrics i.e., fingerprints or iris recognition is not trustworthy in such situations whereas FR is considered as a fine compromise. This survey illustrates different FR practices that laid foundations on the issue of partial occlusion dilemma where faces are disguised to cheat the security system. Occlusion refers to facade of the face image which can be due to sunglasses, hair or wrapping of facial image by scarf or other accessories. Efforts on FR in controlled settings have been in the picture for past several years; however identification under uncontrolled conditions like illumination, expression and partial occlusion is quite a matter of concern. Based on literature a classification is made in this paper to solve the recognition of face in the presence of partial occlusion. These methods are named as part based methods that make use of Principal Component Analysis (PCA), Linear Discriminate Analysis (LDA), Non-negative Matrix Factorization (NMF), Local Non-negative Matrix Factorization (LNMF), Independent Component Analysis (ICA) and other variations. Feature based and fractal based methods consider features around eyes, nose or mouth region to be used in the recognition phase of algorithms. Furthermore the paper details the experiments and databases used by an assortment of authors to handle the problem of occlusion and the results obtained after performing diverse set of analysis. Lastly, a comparison of various techniques is shown in tabular format to give a precise overview of what different authors have already projected in this particular field.
One of the most common and curable types of cancer in women is cervical cancer, a common chronic condition. Pap smear images is a common way for screening the cervical cancer. It does not present with symptoms until the disease has advanced stages, cervical cancer cannot be detected in its early stages. Because of this, accurate staging will make it easier to give the patient the right amount of treatment. In this paper proposes the Anisotropic Diffusion Filter has been used to improve the Pap smear image by removing noise and preserving the image's edges. The contrast of a Pap smear image has been enhanced using Histogram Equalization. The enhanced image has been segmented using Improved Weighted Fuzzy C-means clustering to make it easier to identify the effective features. As a result, the effective features are extracted from the segmented region and used by a Restricted Boltzmann Machine classifier based on Deep Learning to classify the cancer. The performance of the proposed cervical cancer detection system can be measured in terms of sensitivity, specificity, F-measure and accuracy. The performance measures for the proposed system can be achieves 95.3% accuracy, 88.6% specificity, 89.13% precision, 88.56% recall, and 89.7% F-measure respectively. Based on simulation results, the proposed method performs better than conventional methods such as RDVLNN, Random Forest (RF), Extreme Learning Machine (ELM), and Support Vector Machine (SVM) for detecting cervical cancer.
Recent machine learning approaches have been effective in Artificial Intelligence (AI) applications. They produce robust results with a high level of accuracy. However, most of these techniques do not provide human-understandable explanations for supporting their results and decisions. They usually act as black boxes, and it is not easy to understand how decisions have been made. Explainable Artificial Intelligence (XAI), which has received much interest recently, tries to provide human-understandable explanations for decision-making and trained AI models. For instance, in digital agriculture, related domains often present peculiar or input features with no link to background knowledge. The application of the data mining process on agricultural data leads to results (knowledge), which are difficult to explain. In this paper, we propose a knowledge map model and an ontology design as an XAI framework (OAK4XAI) to deal with this issue. The framework does not only consider the data analysis part of the process, but it takes into account the semantics aspect of the domain knowledge via an ontology and a knowledge map model, provided as modules of the framework. Many ongoing XAI studies aim to provide accurate and verbalizable accounts for how given feature values contribute to model decisions. The proposed approach, however, focuses on providing consistent information and definitions of concepts, algorithms, and values involved in the data mining models. We built an Agriculture Computing Ontology (AgriComO) to explain the knowledge mined in agriculture. AgriComO has a well-designed structure and includes a wide range of concepts and transformations suitable for agriculture and computing domains.
Considering that the performance of personality predictors are not consistently increasing throughout the years, as an alternative, the idea was to provide big5 personality traits lexicon based on the Essay dataset. The weight of relevance for every trait of each word is calculated by tf/idf on the words in the Essay dataset. However, it was eventually realized that three personalities wordset were overlapped requiring a change of course. The research changed the underlying model from big5 to Vann Joines’ mental processes as it appear to suit better with the empirical findings. The resulting lexicon dataset is composed with 3432 commonly used words and 3639 avoided prone words for each mental process. The commonly used words are capable of covering 81% of the Twitter Personality dataset.
It is possible for inexpensive cameras to include AI based features such as face recognition. However, a test framework for such cameras is required that will allow comparison of accuracy under differing conditions. This will then lead to the improvement of training data and algorithms. A simple test framework has been developed and partially evaluated by testing multiple head/face accessories under different lighting conditions. Six participants took part and 300 pictures using a Huskylens were taken under a range of conditions. It was found that the camera could detect faces at a reasonable level of accuracy during ‘middle of the day’ lighting conditions, with or without head accessories. However, it delivers significantly lower detection rate with accessories that cover greater parts of the face and under green light. There is still a need to further investigate this area of study with a higher number of participants in a more controlled environment. It is anticipated that better testing frameworks will lead to better algorithms, training data and specifications for users.
In today’s fast-paced world, disabled people are a large minority group, starved of services, mostly ignored by society, and live in isolation, segregation, poverty, charity and even pity. There are numerous forms of disabilities. The disability suffered by most persons includes mental disability, emotional, physical and cognitive. Perhaps the most overlooked effect of a disability that affects the motor functions of the limb is the reliance on other people for the completion of even simple tasks that ordinary people perform on a daily basis, like taking a shower, dressing up, brushing teeth, or even having a meal. This chips away at the self-worth of a disabled person and gnaws away at their confidence. Through our project, we aim to provide a solution to those with compromised motor functions. This project aims to develop a 4 DOF robotic manipulator that is able to map the facial structure of the user, and with a feeding device (spoon/fork) attached to its arm transfers adequate portion of food accurately into the user’s mouth without spillage through smooth motion, by incorporating Image Processing, Manipulator Kinematics and Machine Learning.KeywordsRoboticsKinematicsImage processingMotion planningDesignAutonomousManufacturingHealthcare
The support structures used in additive manufacturing (AM) techniques cause poor surface quality in the contact areas of the support with the printed parts. Thereby, this support structure usage is a critical issue that needs to be controlled for minimizing the printing time and post-processing challenges associated with AM parts. To alleviate the errors due to the support structures, these faceted models can be deposited in multi-direction using multi-axis systems. This paper presents an approach to decompose the design features and detect multiple build directions for certain cases of faceted models to print parts using multi-robots collaborative material extrusion (MRCME) systems developed by the authors. The use of support structures limits the surface finish of as-built 3D printed parts. The objective of this work is to develop a volume decomposition algorithm, which can decompose the part into sub-volumes and identify the build direction of the decomposed sub-volumes. The build direction is found from the geometric reasoning of the concave and convex loop centroids. Initial decomposition is done with the concave–convex loop pair relationship, and further regrouping is done with the bounding box of the identified pair of concave and convex loops in that particular build direction. The work presented in this paper would help process planning systems to automatically determine Multi-Directional Sub-Volumes (MDSV) and carry out effective part deposition with minimum or no support structures.KeywordsMulti-robots technologyMulti-axis depositionCobotic material extrusionPost-processing
The smart manufacturing revolution is continuously enabling the manufacturers to achieve their prime goal of producing more and more products with higher quality at a minimum cost. The crucial technologies driving this new era of innovation are machine learning and artificial intelligence. Paving to the advancements in the digitalization of the production and manufacturing industry and with a lot of available data, various machine learning techniques are employed in manufacturing processes. The main aim of implementing the ML techniques being to save time, cost, resources and avoid possible waste generation. This paper presents a systematic review focusing on the application of various machine learning techniques to different manufacturing processes, mainly welding (arc welding, laser welding, gas welding, ultrasonic welding, and friction stir welding), molding (injection molding, liquid composite, and blow molding) machining (turning, milling, drilling, grinding, and finishing), and forming (rolling, extrusion, drawing, incremental forming, and powder forming). Moreover, the paper also reviews the aim, purpose, objectives, and results of various researchers who have applied AI/ML techniques to a wide range of manufacturing processes and applications.
ABSTRACT: Internet of Things (IoT) Systems consists of electrical and electronic elements and can be used to perform safety functions in industrial and road vehicles. The International Electro-technical Commission (IEC) 61508 is the standard applied for functional safety of Electronic (E) /Electrical (E)/Programmable Electronics (PE) and
other safety related system; whereas ISO 26262 is applicable for road vehicles. IEC 61508 is generic standard which applies to E/E/PE systems irrespective of their application and provides guidelines on product development in compliance with safety features. ISO 26262
follow on standard of IEC 61508 which focus on road safety for electrical or electronic systems. This paper helps to understand key terminologies of these two standards and helps developers to asses SIL/ASIL (Safety Integrity Level/ Automotive Safety Integrity Level) levels. It provides comparison on key metrics calculation between ISO and IEC. If a product developers achieve certain SIL in compliance with IEC standard, it gives and assessment claim to convert to ISO ASIL level and vice versa.
Keywords: Standards; Faults; Safety; Diagnostic coverage; Metrics
Driver drowsiness is the momentous factor in a huge number of vehicle accidents. This driver drowsiness detection system has been valued highly and applied in various fields recently such as driver visual attention monitoring and driver activity tracking. Drowsiness can be detected through the driver face monitoring system. Nowadays smartphone-based application has developed rapidly and thus also used for driver safety monitoring system. In this paper, a detailed review of driver drowsiness detection techniques implemented in the smartphone has been reviewed. The review has also been focused on insight into recent and state-of-the-art techniques. The advantages and limitations of each have been summarized. A comparative study of recently implemented smartphone-based approaches and mostly used desktop-based approaches has also been discussed in this review paper. And the most important thing is this paper helps others to decide better techniques for the effective drowsiness detection.
The volume comprises of papers presented at the first CADEC-2019 conference held at Vellore Institute of Technology-Andhra Pradesh, Amaravati, India. The book contains computer simulated results in various areas of electronics and communication engineering such as, VLSI and embedded systems, wireless communication, signal processing, power electronics and control theory applications. This volume will help researchers and engineers to develop and extend their ideas in upcoming research in electronics and communication.
List of papers appeared in this conference are below:
1. A distinct carry select adder design approach for area and delay reduction using modified full adder by K. Bala Sindhuri, G. S. Chandra Teja, K. Madhusudhan and N. Udaya Kumar
2. Performance analysis of different multiplier architectures using 1-bit full adder structures by N. Udaya Kumar, G. S. Chandra Teja, K. Rajesh, V. Soma Sandeep and K. Bala Sindhuri
3. QCA based binary adder-subtractor by Akondi Narayana Kiran and Akhendra Kumar Padavala
4. Low power design of memoryless adaptive filter using distributed arithmetic unit by Debanjan Kundu and Sriadibhatla Sridevi
5. Design of FINFET based DRAM cell for low power applications by Grande Naga Jyothi, Gorantla Anusha, N. Dilip Kumar and Debanjan Kundu
6. VLSI Architecture of DNN neuron for face recognition by K Rajesh Sai, Plabini Jibanjyoti Nayak and Yallamandaiah S
7. Reconfigurable optimal hybrid vision enhancement system for night surveillance robot using hybrid genetic-PSO algorithm by L.M.I. Leo Joseph and B.Girirajan
8. Eigenface recognition using PCA by K. Anitha, V. Susmitha and M. Srinivasa Rao
9. Sound classification and localization in service robots with attention mechanisms by Matteo Bodini
10. Probabilistic nonlinear dimensionality reduction through gaussian process latent variable models: An overview by Matteo Bodini
11. Comparative study and an improved algorithm for iris and eye corner detection in real time application by Illavarason P, Arokia Renjith J and Mohan Kumar P
12. Review on fast motion estimation algorithms for HEVC in video communication by K. Mohan Kumar, Anirudh P.K.V. and Yallamandaiah S
13. Synchronous multi-port SRAM architectures: A review by G. Saket Kumar, M. Meghana, P. Sri Saranya, S. Yallamandaiah and D. John Pradeep
14. A Closed loop robust controller for SSHI based piezoelectric energy harvester by Sweta Kumari, Subrat Kumar Swain, SitanshuSekhar Sahu, Aditya Kumar, Prashant Kumar and Bharat Gupta
15. Internet of things for wildfire disasters by Gudikandhula Narasimha Rao, P. Jagadeeswara Rao, Rajesh Duvvuru, Kondapalli Beulah and Venkateswarlu Sunkari
16. A fuzzy algorithm for text classification in data science by Kondapalli Beulah, Penmetsa Vamsi Krishna Raja and P. Krishna Subba Rao
17. Face recognition based on local binary pattern-deep belief networks by K. Naga Prakash, K. Prasanthi Jasmine and K. Rasool Reddy
18. Comprehensive analysis of face recognition techniques: A survey by Yallamandaiah S and Purnachand Nalluri
19. Qualitative analysis of emotion recognition systems using deep neural networks by D. Usen and Purnachand. N
20. Deep learning based anomaly detection systems- A review by VishnuPriya Thotakura and Purnachand Nalluri
21. Frequency domain speech bandwidth extension by N. Prasad and P. Akhendra Kumar
22. Design of fractal spiral inductor for wireless applications by Akhendra Kumar Padavala and Prasad Nijampatnam
23. Review on DSP based dynamic gene encoding schemes for the detection of protein coding region by Raman Kumar M and Vaegae Naveen Kumar
24. Damaged video reconstruction using inpainting by Kamesh Sonti and K. Rasool Reddy
25. Investigation in reduction of radar cross section due to plasma release from target in active stealth technology by Swathi Nambari, Sasibhushana Rao Gottapu and Kolluri Sri Ranga Rao
26. Performance comparison of microstrip antenna and dielectric resonator antenna (DRA) at RFID application by K. Rama Devi
27. A study of poultry realtime monitoring and automation techniques by Arun Gnana Raj, S. Margaret Amala and J. Gnana Jayanthi
28. Impact of MgO interfacial layer of gate dielectric engineered monolayer MoS2 FET by Divya Bharathi N. and Sivasankaran K.
29. Achieving ISO 26262 & IEC 61508 objectives with a common development process by Gadila Prashanth Reddy, Rangaiah Leburu, Kankanala Rajireddy, Jayakrishnan P and Justin Khoo
30. Comparative analysis of impedance and capacitance based bio-sensors for cellular pathology by Syed Shameem, P.S. Srinivas Babu, H. Sri Varun, C. Renukavalli and B. Manasa
31. Simulation of GaN MOS-HEMT based bio-sensor for breast cancer detection by Rohit Bhargav Peesa, Pydimarri Manoj Kumar and D.K. Panda
32. Development of charge and discharge controller for solar lighting system by Jayapragash R, Nandeesh Kumar K, Thirumalvalavan G and Arul R
33. ASIC Design of ALU with different multipliers by Ramadevi Vemula and K. Manjunatha Chari
34. Gate diffusion input (Gdi) technique based CAM cell design for low power and high performance by S.V.V. Satyanarayana, Sridevi Sriadibhatla and Nannuru Amarnath
Image Denoising is one of the fundamental and very important necessary processes in image processing. It is still a
challenging and a hot problem for researchers. Images are one of essential representations in every field like education,
agriculture, geosciences, aerospace, surveillance, entertainment etc by means of electronic or print media. Images can get
corrupted by noise, there has been a great research effort which made solutions for this problem, a number of methods
have been proposed. An overview of various methods is given here after a brief introduction. These methods have been
categorized on the bases of techniques used.
Face detection and recognition are major concerns in the area of biometric based security systems and purposes. These processes must ensure the recognition accuracy and minimum processing time. In this paper a review of existing face detection and recognition approaches is conducted to investigate the results of different approaches in terms of recognition accuracy and some of them are discussed for minimizing processing time point of view. The techniques for face detection and recognition are classified on the bases of their target application. Also, the techniques are classified and analyzed on the bases of their working domain as spatial, frequency, integrated and hardware support.
This paper deals with the tracking system of the human face and facial feature based on the human face texture model combined with an algorithm for adaptation of the wireframe 3D model Candide-3 to the human face images. The human face texture model is represented by a set of eigenfaces which are obtained by means of the principal component analysis of the training set of completely preprocessed textures. The algorithm for adaptation of 3D model needs a reasonable starting approximation and an update matrix calculated from the training set by manual deviation of 3D model for single components of the parameter vector. The designed tracking system was tested on a real videosequence with various conditions and for adaptation of 3D model both global motion parameters and animation parameters of the mouth were used.
We develop a face recognition algorithm which is insensitive to gross variation in lighting direction and facial expression. Taking a pattern classification approach, we consider each pixel in an image as a coordinate in a high-dimensional space. We take advantage of the observation that the images of a particular face under varying illumination direction lie in a 3-D linear subspace of the high dimensional feature space — if the face is a Lambertian surface without self-shadowing. However, since faces are not truly Lambertian surfaces and do indeed produce self-shadowing, images will deviate from this linear subspace. Rather than explicitly modeling this deviation, we project the image into a subspace in a manner which discounts those regions of the face with large deviation. Our projection method is based on Fisher's Linear Discriminant and produces well separated classes in a low-dimensional subspace even under severe variation in lighting and facial expressions. The Eigenface technique, another method based on linearly projecting the image space to a low dimensional subspace, has similar computational requirements. Yet, extensive experimental results demonstrate that the proposed Fisherface method has error rates that are significantly lower than those of the Eigenface technique when tested on the same database.
Face is a more common and important biometric identifier for recognizing a person in a non-invasive way. The face recognition involves identification of the facial features, namely, eyes, eyebrows, nose, mouth, ears, and their spatial interrelationships uniquely. The variability in the facial features of the same human face due to changes in facial expressions, illumination and poses shall not alter the face recognition. In the present chapter we have discussed the modeling of the uncertainty in information about facial features for face recognition under varying face expressions, poses and illuminations. There are two approaches, namely, fuzzy face model based on fuzzy geometric rules and symbolic face model based on extension of symbolic data analysis to PCA and its variants. The effectiveness of these approaches is demonstrated by the results of extensive experimentation using various face databases, namely, ORL, FERET, MIT-CMU and CIT. The fuzzy face model as well as symbolic face model are found to capture variability of facial features adequately for successful face detection and recognition.
As one of the most successful applications of image analysis and understanding, face recognition has recently received significant attention, especially during the past several years. This is evidenced by the emergence of face recognition conferences such as AFGR [1] and AVBPA [2], and systematic empirical evaluations of face recognition techniques, including the FERET [3, 4, 5, 6] and XM2VTS [7] protocols. There are at least two reasons for this trend; the first is the wide range of commercial and law enforcement applications, and the second is the availability of feasible technologies after 30 years of research. This paper provides an up-to-date critical survey of still- and video-based face recognition research. 1 The support of the Office of Naval Research under Grants N00014-95-1-0521 and N00014-00-1-0908 is gratefully acknowledged. 2 Vision Technologies Lab, Sarnoff Corporation, Princeton, NJ 08543-5300. 3 Center for Automation Research, University of Maryland, College Park...
Problem statement: Template matching had been a conventional method for object detection especially facial features detection at the early stage of face recognition research. The appearance of moustache and beard had affected the performance of features detection and face recognition system since ages ago. Approach: The proposed algorithm aimed to reduce the effect of beard and moustache for facial features detection and introduce facial features based template matching as the classification method. An automated algorithm for face recognition system based on detected facial features, iris and mouth had been developed. First, the face region was located using skin color information. Next, the algorithm computed the costs for each pair of iris candidates from intensity valleys as references for iris selection. As for mouth detection, color space method was used to allocate lips region, image processing methods to eliminate unwanted noises and corner detection technique to refine the exact location of mouth. Finally, template matching was used to classify faces based on the extracted features. Results: The proposed method had shown a better features detection rate (iris = 93.06%, mouth = 95.83%) than conventional method. Template matching had achieved a recognition rate of 86.11% with acceptable processing time (0.36 sec). Conclusion: The results indicate that the elimination of moustache and beard has not affected the performance of facial features detection. The proposed features based template matching has significantly improved the processing time of this method in face recognition research.
A number of current face recognition algorithms use face representations found by unsupervised statistical methods. Typically these methods find a set of basis images and represent faces as a linear combination of those images. Principal component analysis (PCA) is a popular example of such methods. The basis images found by PCA depend only on pairwise relationships between pixels in the image database. In a task such as face recognition, in which important information may be contained in the high-order relationships among pixels, it seems reasonable to expect that better basis images may be found by methods sensitive to these high-order statistics. Independent component analysis (ICA), a generalization of PCA, is one such method. We used a version of ICA derived from the principle of optimal information transfer through sigmoidal neurons. ICA was performed on face images in the FERET database under two different architectures, one which treated the images as random variables and the pixels as outcomes, and a second which treated the pixels as random variables and the images as outcomes. The first architecture found spatially local basis images for the faces. The second architecture produced a factorial face code. Both ICA representations were superior to representations based on PCA for recognizing faces across days and changes in expression. A classifier that combined the two ICA representations gave the best performance.
Human face detection plays an important role in applications such
as video surveillance, human computer interface, face recognition, and
face image database management. We propose a face detection algorithm
for color images in the presence of varying lighting conditions as well
as complex backgrounds. Based on a novel lighting compensation technique
and a nonlinear color transformation, our method detects skin regions
over the entire image and then generates face candidates based on the
spatial arrangement of these skin patches. The algorithm constructs eye,
mouth, and boundary maps for verifying each face candidate. Experimental
results demonstrate successful face detection over a wide range of
facial variations in color, position, scale, orientation, 3D pose, and
expression in images from several photo collections (both indoors and
outdoors)
We have developed a near-real-time computer system that can locate and track a subject's head, and then recognize the person by comparing characteristics of the face to those of known individuals. The computational approach taken in this system is motivated by both physiology and information theory, as well as by the practical requirements of near-real-time performance and accuracy. Our approach treats the face recognition problem as an intrinsically two-dimensional (2-D) recognition problem rather than requiring recovery of three-dimensional geometry, taking advantage of the fact that faces are normally upright and thus may be described by a small set of 2-D characteristic views. The system functions by projecting face images onto a feature space that spans the significant variations among known face images. The significant features are known as "eigenfaces," because they are the eigenvectors (principal components) of the set of faces; they do not necessarily correspond to features such as eyes, ears, and noses. The projection operation characterizes an individual face by a weighted sum of the eigenface features, and so to recognize a particular face it is necessary only to compare these weights to those of known individuals. Some particular advantages of our approach are that it provides for the ability to learn and later recognize new faces in an unsupervised manner, and that it is easy to implement using a neural network architecture.
The paper proposes a novel algorithm to dynamically define the Region of Interest (ROI) videophone application. The algorithm uses the color information Hue and Cr to find the skin-color pixels and also use range of threshold obtained from red and blue components in normalized RGB color space to remove nonskin-color pixels because the human skin tends to have a predominance of red and non-predominance of blue. Post-processing is used to remove such noises by a morphological operator. Moreover, the algorithm performs temporal filtering to remove skin-color pixels that immediately appear from frame to frame by using object tracking process to perform as memory for collecting skin-color objects obtained from previous frame to guide the next frame. The experimental results confirm the effectiveness of the proposed algorithm. Keywords—face segmentation, human skin segmentation, video object segmentation, Region-of-Interest (ROI) video coding.
Over the last two decades, several different techniques have been proposed for face recognition, which is one of the challenging areas of research in the field of image processing, pattern recognition and vision applications. Automatic human face identification system, e.g. security checks and crime investigation, etc. involves face recognition. The basic process consists of extraction of potential, facial features such as eyes, nose, mouth, eyebrows, etc. In the present paper, a geometrical face model proposed by Shi-Hong Jeng et al. for frontal face images is improved by the inclusion of ears and chin also as potential facial features, since it enhances the discrimination ability of the proposed face model during face recognition .The developed approach is divided into four main steps. The first step is pre processing, the goal of this step is to get rid of high intensity noises and to transform the input image into binary one. The second step includes a labeling process, which label the facial feature candidates by block by block. Then find the center, area and the orientation of each feature candidate. Third step is a geometrical model, used to measure relative distances and to locate the actual position of the entire facial features. Finally, the matching process. The modified face model has been experimented with test images and an enhanced success rate of 94% is achieved
In this paper we present a biometric system of face detection and recognition in color images. The face detection technique is based on skin color information. A new algorithm is proposed in order to detect automatically face features (eyes, mouth and nose) and extract their correspondent geometrical points. These fiducial points are described by sets of wavelet components called "jets" which are used for recognition. To achieve the face recognition, we propose two architectures of neural networks and we compare their performances. We also, compare the two types of features used for recognition: geometric distances and Gabor coefficients which can be used either independently or jointly. This comparison shows that Gabor coefficients are more powerful than geometric distances. We show with experimental results how the importance recognition ratio makes our system an effective tool for automatic face detection and recognition.
A face recognition system based on 2D DCT features and pseudo 2D Hidden Markov Models is presented. An extension of the system is capable of recognizing faces by using JPEG compressed image data. Experiments to evaluate the proposed approach are carried out on the Olivetti Research Laboratory (ORL) face database. The recognition rates are 100% for the uncompressed original images and 99.5% for JPEG compressed domain recognition. A comparison with other face recognition systems evaluated on the ORL database, shows that these are the best recognition results ever reported on this database.
A method for detecting and describing the features of faces using deformable templates is described. The feature of interest, an eye for example, is described by a parameterized template. An energy function is defined which links edges, peaks, and valleys in the image intensity to corresponding properties of the template. The template then interacts dynamically with the image, by altering its parameter values to minimize the energy function, thereby deforming itself to find the best fit. The final parameter values can be used as descriptors for the features. This method is demonstrated by showing deformable templates detecting eyes and mouths in real images
This letter describes a high-performance face recognition system by combining two recently proposed neural network models, namely Gabor wavelet network (GWN) and kernel associative memory (KAM), into a unified structure called Gabor wavelet associative memory (GWAM). GWAM has superior representation capability inherited from GWN and consequently demonstrates a much better recognition performance than KAM. Extensive experiments have been conducted to evaluate a GWAM-based recognition scheme using three popular face databases, i.e., FERET database, Olivetti-Oracle Research Lab (ORL) database and AR face database. The experimental results consistently show our scheme's superiority and demonstrate its very high-performance comparing favorably to some recent face recognition methods, achieving 99.3% and 100% accuracy, respectively, on the former two databases, exhibiting very robust performance on the last database against varying illumination conditions.
Elastic graph matching has been proposed as a practical implementation of dynamic link matching, which is a neural network with dynamically evolving links between a reference model and an input image. Each node of the graph contains features that characterize the neighborhood of its location in the image. The elastic graph matching usually consists of two consecutive steps, namely a matching with a rigid grid, followed by a deformation of the grid, which is actually the elastic part. The deformation step is introduced in order to allow for some deformation, rotation, and scaling of the object to be matched. This method is applied here to the authentication of human faces where candidates claim an identity that is to be checked. The matching error as originally suggested is not powerful enough to provide satisfying results in this case. We introduce an automatic weighting of the nodes according to their significance. We also explore the significance of the elastic deformation for an application of face-based person authentication. We compare performance results obtained with and without the second matching step. Results show that the deformation step slightly increases the performance, but has lower influence than the weighting of the nodes. The best results are obtained with the combination of both aspects. The results provided by the proposed method compare favorably with two methods that require a prior geometric face normalization, namely the synergetic and eigenface approaches.
Optimized geometrical feature vector for face recognition
- P S Hiremath
- D Ajit
Hiremath, P.S. and D. Ajit, 2004. Optimized geometrical feature vector for face recognition. Proceedings of the International Conference on Human Machine Interface, Indian Institute of Science, Tata McGraw-Hill, Bangalore, pp: 309-320, (ISBN: 0 07- 059757- X)
Automatic facial features extraction for face recognition by neural networks Face segmentation using novel skin-colour map and morphological technique Eigenfaces for recognition
- S Khanfir
- Y B Jemaa
- Tunisia Sawangsri
- V Patanavijit
- S S Jitapunkul
Khanfir, S. and Y.B. Jemaa, 2006. Automatic facial features extraction for face recognition by neural networks, 3rd International Symposium on Image/Video Communications over fixed and mobile networks (ISIVC), Tunisia. Sawangsri, T., V. Patanavijit and S.S. Jitapunkul, 2004. Face segmentation using novel skin-colour map and morphological technique. Trans. Engine., Comp. Technol., 2. Turk, M. and A. Pentland, 1991. Eigenfaces for recognition. J. Cogn. Neurosci., 3(1): 71-86. Wiskott, L., J.M. Fellous, N. Kruger and C.V.D
Face Recognition by Elastic Bunch Graph Matching. Intelligent Biometric Techniques in Fingerprint and Face Recognition
- L Wiskott
- J M Fellous
- N Kruger
- C V D Malsburg
Wiskott, L., J.M. Fellous, N. Kruger and C.V.D.
Malsburg, 1999. Face Recognition by Elastic Bunch
Graph Matching. Intelligent Biometric Techniques in
Fingerprint and Face Recognition, Chapter 11, pp:
355-396.