Conference Paper

Facial recognition system for security access control

Authors:
To read the full-text of this research, you can request a copy directly from the authors.

No full-text available

Request Full-text Paper PDF

To read the full-text of this research,
you can request a copy directly from the authors.

ResearchGate has not been able to resolve any citations for this publication.
Conference Paper
Full-text available
Annotating a qualitative large-scale facial expression dataset is extremely difficult due to the uncertainties caused by ambiguous facial expressions, low-quality facial images, and the subjectiveness of annotators. These uncertainties lead to a key challenge of large-scale Facial Expression Recognition (FER) in deep learning era. To address this problem, this paper proposes a simple yet efficient Self-Cure Network (SCN) which suppresses the uncertainties efficiently and prevents deep networks from over-fitting uncertain facial images. Specifically, SCN suppresses the uncertainty from two different aspects: 1) a self-attention mechanism over mini-batch to weight each training sample with a ranking regularization, and 2) a careful relabeling mechanism to modify the labels of these samples in the lowest-ranked group. Experiments on synthetic FER datasets and our collected WebEmotion dataset validate the effectiveness of our method. Results on public benchmarks demonstrate that our SCN outperforms current state-of-the-art methods with 88.14% on RAF-DB, 60.23% on AffectNet, and 89.35% on FERPlus.
Conference Paper
Full-text available
This paper addresses the problem of Face Alignment for a single image. We show how an ensemble of regression trees can be used to estimate the face's landmark positions directly from a sparse subset of pixel intensities, achieving super-realtime performance with high quality predictions. We present a general framework based on gradient boosting for learning an ensemble of regression trees that optimizes the sum of square error loss and naturally handles missing or partially labelled data. We show how using appropriate priors exploiting the structure of image data helps with ef-ficient feature selection. Different regularization strategies and its importance to combat overfitting are also investi-gated. In addition, we analyse the effect of the quantity of training data on the accuracy of the predictions and explore the effect of data augmentation using synthesized data.
Article
Full-text available
Face recognition is a widely used biometric approach. Face recognition technology has developed rapidly in recent years and it is more direct, user friendly and convenient compared to other methods. But face recognition systems are vulnerable to spoof attacks made by non-real faces. It is an easy way to spoof face recognition systems by facial pictures such as portrait photographs. A secure system needs Liveness detection in order to guard against such spoofing. In this work, face liveness detection approaches are categorized based on the various types techniques used for liveness detection. This categorization helps understanding different spoof attacks scenarios and their relation to the developed solutions. A review of the latest works regarding face liveness detection works is presented. The main aim is to provide a simple path for the future development of novel and more secured face liveness detection approach.
Article
Full-text available
Inferring scene geometry and camera motion from a stream of images is possible in principle, but is an ill-conditioned problem when the objects are distant with respect to their size. The authors have developed a factorization method that can overcome this difficulty by recovering shape and motion under orthography without computing depth as an intermediate step. An image stream can be represented by the measurement matrix of the image coordinates of points tracked through frames. The authors show that under orthographic projection this matrix is of rank 3. Based on this observation, the factorization method uses the singular-value decomposition technique to factor the measurement matrix into two matrices which represent object shape and camera rotation respectively. Two of the three translation components are computed in a preprocessing stage. The method can also handle and obtain a full solution from a partially filled-in measurement matrix that may result from occlusions or tracking failures. The method gives accurate results, and does not introduce smoothing in either shape or motion. The authors demonstrate this with a series of experiments on laboratory and outdoor image streams, with and without occlusions
Conference Paper
Full-text available
This paper describes a machine learning approach for visual object detection which is capable of processing images extremely rapidly and achieving high detection rates. This work is distinguished by three key contributions. The first is the introduction of a new image representation called the "integral image" which allows the features used by our detector to be computed very quickly. The second is a learning algorithm, based on AdaBoost, which selects a small number of critical visual features from a larger set and yields extremely efficient classifiers. The third contribution is a method for combining increasingly more complex classifiers in a "cascade" which allows background regions of the image to be quickly discarded while spending more computation on promising object-like regions. The cascade can be viewed as an object specific focus-of-attention mechanism which unlike previous approaches provides statistical guarantees that discarded regions are unlikely to contain the object of interest. In the domain of face detection the system yields detection rates comparable to the best previous systems. Used in real-time applications, the detector runs at 15 frames per second without resorting to image differencing or skin color detection.
We study the question of feature sets for robust visual object recognition, adopting linear SVM based human detection as a test case. After reviewing existing edge and gradient based descriptors, we show experimentally that grids of Histograms of Oriented Gradient (HOG) descriptors significantly outperform existing feature sets for human detection. We study the influence of each stage of the computation on performance, concluding that fine-scale gradients, fine orientation binning, relatively coarse spatial binning, and high-quality local contrast normalization in overlapping descriptor blocks are all important for good results. The new approach gives near-perfect separation on the original MIT pedestrian database, so we introduce a more challenging dataset containing over 1800 annotated human images with a large range of pose variations and backgrounds.
New method for subject identification based on palm print
  • H I Mejía
  • R A Chiclayo
  • N Florez
  • V Tuesta
  • M G Forero
Mejía, H. I., Chiclayo, R. A., Florez, N., Tuesta, V. and Forero, M. G., "New method for subject identification based on palm print", Proc. SPIE 11510, Applications of Digital Image Processing XLIII, 115100L (21 August 2020); <https://doi.org/10.1117/12.2567196>
The Influence of the Activation Function in a Convolution Neural Network Model of Facial Expression Recognition
  • Y Wang
  • Y Li
  • Y Song
  • X Rong
Wang, Y., Li, Y., Song, Y. and Rong, X. "The Influence of the Activation Function in a Convolution Neural Network Model of Facial Expression Recognition." Appl. Sci. 2020, 10, 1897. <https://doi.org/10.3390/app10051897>
  • A Geitgey
Geitgey, A. "Machine Learning is Fun! Part 4: Modern Face Recognition with Deep Learning. Medium", Medium,14 July 2016, < https://medium.com/@ageitgey/machine-learning-is-fun-part-4-modern-face-recognition-withdeep-learning-c3cffc121d78 > (14 July 2016)
Part 3 -Lucas-Kanade Optical Flow
  • A Sehgal
Sehgal, A., "Part 3 -Lucas-Kanade Optical Flow", Medium, 17 May 2020, <https://medium.com/buildingautonomous-flight-software/lucas-kanade-optical-flow-942d6bc5a078>, (2020)
How to Detect Mouth Open for Face Login
  • P Xie
Xie, P., "How to Detect Mouth Open for Face Login", Towards data science,8 October 2019, <https://towardsdatascience.com/how-to-detect-mouth-open-for-face-login-84ca834dff3b>, (8 October 2019)
Real-Time Eye Blink Detection using Facial Landmarks
  • T Soukupová
  • J Cech
Soukupová, T. and Cech, J., "Real-Time Eye Blink Detection using Facial Landmarks.", Semantic scholar, 2016, < https://www.semanticscholar.org/paper/Real-Time-Eye-Blink-Detection-using-Facial-Soukupov%C3%A1-Cech/ 4fa1ba3531219ca8c39d8749160faf1a877f2ced>, (2016)
Eye blink detection with OpenCV, Python, and dlib
  • A Rosebrock
Rosebrock, A., "Eye blink detection with OpenCV, Python, and dlib", Pyimagesearch, 24 April 2017, <https://www.pyimagesearch.com/2017/04/24/eye-blink-detection-opencv-python-dlib/>, (24 April 2017)
Liveness Detection with OpenCV
  • A Rosebrock
Rosebrock, A., "Liveness Detection with OpenCV", Pyimagesearch, 11 March 2019, <https://www.pyimagesearch.com/2019/03/11/liveness-detection-with-opencv/>, (11 March 2019)