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Examples of physiological/biological and behavioral traits applied in biometric recognition applications.
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With the proliferation of automated systems for reliable and highly secure human authentication and identification, the importance of technological solutions in biometrics is growing along with security awareness. Indeed, conventional authentication methodologies, consisting of knowledge-based systems that make use of something you know (e.g., user...
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... biometric modalities have been developed over the years making the biometric technology landscape very vibrant. Prominent examples of physiological/ biological and behavioral biometric characteristics, which have been the purpose of major real-world applications, are illustrated in Figure 1. ...
Citations
... Biometric characteristics are distinguished by unique features and patterns that enable the identi�ication and veri�ication of individuals, offering a higher level of security compared to traditional methods such as passwords or PIN codes [2]. Palm vein recognition systems operate by analysing the vein structures beneath the skin of the palm. ...
A major challenge with palm vein images is that slight movements of the fingers and thumb, or variations in hand posture, can stretch the skin in different areas and alter the vein patterns. This can result in an infinite number of variations in palm vein images for a given individual. This paper introduces a novel filtering technique for SIFT-based feature matching, known as the Mean and Median Distance (MMD) Filter. This method evaluates the differences in keypoint coordinates and computes the mean and median in each direction to eliminate incorrect matches. Experiments conducted on the 850nm subset of the CASIA dataset indicate that the proposed MMD filter effectively preserves correct points while reducing false positives detected by other filtering methods. A comparison with existing SIFT-based palm vein recognition systems demonstrates that the proposed MMD filter delivers outstanding performance, achieving lower Equal Error Rate (EER) values. This article presents an extended author's version based on our previous work, A Keypoint Filtering Method for SIFT based Palm-Vein Recognition.
... Traditional authentication methods, such as PIN codes and passwords, have long been recognised as inef�icient and insecure. In contrast, biometric recognition offers signi�icantly more secure authentication by utilising one or more physiological traits [1]. Vascular biometric recognition systems identify individuals based on the unique patterns of blood vessels beneath the skin. ...
... Figure 4). v 1 and v 2 represent common vertical edges, while h 1 and h 2 represent common horizontal edges. C 1 is the common vertex across all four tiles. ...
... The value of the Bʹ 1 pixel is not in�luenced by tile Cʹ, and the value of the Bʹ 2 pixel is not in�luenced by tile Bʹ. Cʹ 1 and Bʹ 2 are affected by both overlapping tiles. ...
This article presents an extended author's version based on our previous work, where we introduced the Multiple Overlapping Tiles (MOT) method for palm vein image enhancement. To better reflect the specific operations involved, we rename MOT to ILACS-LGOT (Intensity-Limited Adaptive Contrast Stretching with Layered Gaussian-weighted Overlapping Tiles). This revised terminology more accurately represents the method's approach to contrast enhancement and blocky effect mitigation. Additionally, this article provides a more detailed analysis, including expanded evaluations, graphical representations, and sample-based comparisons, demonstrating the effectiveness of ILACS-LGOT over existing methods.
... This process results in the formation of a secure and virtually inviolable biometric template [4]. The necessity for a live individual's presence during the scanning procedure further strengthens the system defences against cyber threats such as spoofing and tampering, except when a sensor module is compromised [5]. Unlike facial recognition, which can be fooled by photographs or videos, or fingerprint scanners, which can be deceived with artificial replicas, the retina is virtually impossible to replicate convincingly. ...
... Storing these biometric templates without proper security exposes them to a range of threats, making them susceptible to unauthorised access and potential misuse. However, it's essential to recognise that a biometric security solution comprises multiple components, with the recognition module addressing authentication as just one aspect (see [5] for a comprehensive overview of BAS vulnerabilities). ...
... The point where rejection and acceptance errors are equal is referred to as the Equal Error Rate (EER). Furthermore, the ROC and DET curves are threshold-independent, enabling the comparison of performance across different biometric systems under similar conditions [5]. ...
Securing personal information and data has become an imperative challenge, especially after the introduction of legal frameworks, such as, in Europe, the General Data Protection Regulation (GDPR). Conventional authentication methods, such as PINs and passwords, have demonstrated their vulnerabilities to various cyber threats, making it necessary the study of robust and reliable biometric authentication systems that can accurately verify an user’s identity. The human retina has demonstrated remarkable reliability as a biometric trait mainly because of its unique and stable patterns, even though the adoption of these systems gives rise to significant concerns regarding the confidentiality of biometric data. This study presents a groundbreaking approach to address these concerns by integrating homomorphic encryption into retina-based authentication. The combination of homomorphic encryption and retina biometrics within the proposed framework offers a comprehensive solution that ensures both privacy and security with no loss in accuracy. The proposed approach mitigates the risks associated with possible unauthorised access and security breaches by keeping the data encrypted throughout the entire procedure. Furthermore, it preserves the individual’s privacy by preventing the exposure of sensitive biometric information. We evaluated the proposed system through extensive experiments and simulations, demonstrating its effectiveness in terms of both security and privacy when the system operates in normal (ideal) and abnormal (under attack) conditions. Experimental results indicate that the combined approach offers robust resistance to various attacks, including replay attacks and data exposure, providing a robust and privacy-centric authentication solution.
... Biometric recognition technology is an authentication method that leverages the unique biological characteristics of users, such as fingerprints, facial features, and irises [5]. This approach eliminates the need for users to explicitly input authentication information, resulting in expedited authentication processes; however, it remains susceptible to numerous security vulnerabilities. ...
With the ongoing advancements in computer technology and mobile internet, mobile payment has become an integral component of daily life. Identity authentication technology serves as a critical measure to ensure the security of mobile payments. This paper addresses the issue of low security associated with single-factor identity authentication while highlighting the substantial overhead involved in multi-factor authentication methods. We propose a novel identity authentication method that integrates biometric data and geographic information. In this framework, the identity authentication server first performs system initialization, configures system parameters, and stores them securely. Subsequently, the identity authentication client submits an authentication request to the server and completes its registration process. Finally, the server processes this request from the client, conducts user identity verification, and returns an authentication result. This approach effectively mitigates risks posed by attackers attempting to falsify biometric data or geographic information during the identity verification process and offers robust services for biometric- and geography-based identity authentication.
... These systems automatically recognize a person's identity by comparing biometric traits [6] with the data stored in the database. As an authentication system, a biometric system can determine whether the recognition result is valid or not [7], whether to accept or reject, and whether the user is recognized or not recognized [8] because basically, a face recognition system works by comparing the input image with an image that has been stored in a database and finding the face match that best matches the existing input data [9]. ...
The presence of employees is a key factor in supporting the needs of the workplace. At present, the employee presence system at PT. AirNav Indonesia Semarang Branch still uses fingerprint and RFID-based employee ID cards for authentication. This RFID-based system can increase employee fraud by allowing employees to misuse each other's ID cards. To avoid such fraud, a system needs to be built and it will be using face recognition technology as the primary authentication method, with the Haar Cascade Algorithm. This algorithm has the advantage of being computationally fast, as it only relies on the number of pixels within a rectangle, not every pixel of an image. In addition to fast computation, this algorithm also has the advantage of identifying objects that are relatively far away. With the implementation of the Haar Cascade algorithm, the results indicate the capability of face recognition in detecting the faces of registered employees within the system based on facial angles with an accuracy rate of 60%, expressions with an accuracy rate of 100%, as well as obstructive parameters such as glasses and masks with an accuracy rate of 33.33%. The ability to detect objects from various camera angles, recognize faces with different expressions, and identify objects obstructed by parameters can serve as reasons why this algorithm needs to be implemented
... Maintaining the security of confidential information and personal information as well as the ability to securely access them is of particular importance, so defining appropriate solutions for this purpose is very necessary and essential. One of these reliable solutions is the use of physiological features and characteristics, which are called biometrics and have a higher level of reliability and security [1]. In biometric systems, the vital and behavioral characteristics of people are used to recognize their identity. ...
Today, advancements in science and technology have spurred the rapid evolution of systems like electronic banking, demanding precise, swift, and secure identification of individuals based on their distinct traits. Among these traits, fingerprints stand out as a dependable means of identification, finding application in realms such as crime investigation and national border control due to their simplicity and heightened security. The qualities inherent in fingerprint-based identification have led to its widespread adoption over other identification methods. This article proposes a hybrid biometric system that integrates the Gabor filter and scale-invariant feature transform features and then uses support vector machine and K-nearest neighbors as classifiers, aiming to notably enhance authentication systems by mitigating issues seen in single-method biometric systems. Also, principal component analysis is used to reduce dimensions and eliminate redundancy. In this article, the famous database FVC2004 is used. Test results highlight the considerable reliability and accuracy of the proposed combined approach compared to systems reliant on a singular biometric method.
... Forensics and DNA applications were presented in [13]. An overview based on human recognition systems was provided in [14]. ...
DNA, a significant physiological biometric, is present in all human cells like hair, blood, and skin. This research introduces a new approach called the Deep DNA Learning Network (DDLN) for person identification based on their DNA. This novel Machine Learning model is designed to gather DNA chromosomes from an individual's parents. The model's flexibility allows it to expand or contract and has the capability to determine one or both parents of an individual using the provided chromosomes. Notably, the DDLN model offers quick training in comparison to traditional deep learning methods. The study employs two real datasets from Iraq: the Real Iraqi Dataset for Kurds (RIDK) and the Real Iraqi Dataset for Arabs (RIDA). The outcomes demonstrate that the proposed DDLN model achieves an Equal Error Rate (EER) of 0 for both datasets, indicating highly accurate performance.
... Signal processing was utilized to condense DNA sequences [12]. While, the DNA typing based on forensics was discussed in [13], [14]. Further new studies are provided for different DNA applications [15]- [21]. ...
One of the most significant physiological biometrics is the deoxyribonucleic acid (DNA). It can be found in every human cell as in hair, blood, and skin. In this paper, a special DNA deep learning (SDDL) is proposed as a novel machine learning (ML) model to identify persons depending on their DNAs. The proposed model is designed to collect DNA chromosomes of parents for an individual. It is flexible (can be enlarged or reduced) and it can identify one or both parents of a person, based on the provided chromosomes. The SDDL is so fast in training compared to other traditional deep learning models. Two real datasets from Iraq are utilized called: Real Iraqi Dataset for Kurd (RIDK) and Real Iraqi Dataset for Arab (RIDA). The results yield that the suggested SDDL model achieves 100% testing accuracy for each of the employed datasets.
... For instance, the FBI uses a single form that can identify eight distinct types of patterns, such as accidental, radial loop, ulnar loop, double loop, central pocket loop, plain arch, tented arch, plain whorl, and radial whorl patterns. This form may also be used to determine whether or not a pattern is a radial whorl pattern [1]. Frequently, a جملة كلية الرتبية االساسية شباط ) 4246 ( February 54 whorl has a circular or spiral configuration. ...
... By utilizing image processing, one can extract fingerprint features using one of about four main types of techniques [11]. Without using the binarization and thinning processes, the first category of approaches directly pulls information from the gray-level picture [1,23,25,34], while the second group of methods derives features from binary image profile patterns [15,25,26]. The omission of the word "process" distinguishes both types of approaches. ...
The distinctive patterns observed on human fingertips are a result of the combination of ridges and troughs present on the skin surface. These patterns achieve their complete development during the period of pregnancy and remain stable throughout an individual's lifespan. Fingerprints refer to the impressions formed by the unique patterns present on the fingertips. Bruising resulting from accidental injuries, such as cuts and burns, can temporarily compromise the visibility of fingerprints. However, after the lesion has fully healed, the original patterns will be restored. Fingerprint recognition is a highly dynamic and extensively researched domain within the realm of biometrics. The word refers to the abbreviated form of the computational technique employed to ascertain the concordance between two distinct human fingerprints. In this naturally tough pattern recognition issue, it is imperative to minimize two error rates: the False Accept Rate (FAR) and the False Reject Rate (FRR). These mistake rates are in competition with each other. The development of computing capabilities has facilitated the creation of Automated Fingerprint Authentication Systems (AFIS), leading to a substantial increase in research activity, particularly in the past twenty years. This article aims to provide a comprehensive review of the issue surrounding fingerprint recognition, including an analysis of its underlying design and implementation difficulties, along with an assessment of its potential future developments.
... In the coming subsections, we denote the results for the proposed methods. The False Rejection Ratio (FRR) and the False Acceptance Ratio (FAR) parameters were calculated, thanks to which the Equal Error Ratio (EER) was computed [23]. The False Match Rate (FMR) is the rate at which a biometric process mismatches biometric signals from two distinct individuals as coming from the same individual. ...
Mobile devices have become integral to daily life, necessitating robust user authentication methods to safeguard personal information. In this study, we present a new approach to mobile user authentication utilizing ear-touch interactions. Our novel system employs an analytical algorithm to authenticate users based on features extracted from ear-touch images. We conducted extensive evaluations on a dataset comprising ear-touch images from 92 subjects, achieving an average equal error rate of 0.04, indicative of high accuracy and reliability. Our results suggest that ear-touch-based authentication is a feasible and effective method for securing mobile devices.
