The Interplay of AI and Biometrics: Challenges and Opportunities (original) (raw)
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Throughout the last 40 years, the essence of automated identification of users has remained the same. In this article, a new class of biometrics is proposed that is founded on processing biosignals, as opposed to images. After a brief introduction on biometrics, biosignals are discussed, including their advantages, disadvantages, and guidelines for obtaining them. This new class of biometrics increases biometrics' robustness and enables cross validation. Next, biosignals' use is illustrated by two biosignal-based biometrics: voice identification and handwriting recognition. Additionally, the concept of a digital human model is introduced. Last, some issues will be touched upon that will arise when biosignal-based biometrics are brought to practice.
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The use of biometrics has been successfully applied to security applications for some time. However, the extension of other potential applications with the use of biometric information is a very recent development. This paper summarizes the field of biometrics and investigates the potential of utilizing biometrics beyond the presently limited field of security applications. There are some synergies that can be established within security-related applications. These can also be relevant in other fields such as health and ambient intelligence. This paper describes these synergies. Overall, this paper highlights some interesting and exciting research areas as well as possible synergies between different applications using biometric information.
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Volume 29, Issue 3, 2018
Identification and access have been a concept that has evolved over time as the need to constantly identify people and grant them access to sensitive and classified data and information became very important. The effect is felt in most organizations, especially multinational companies that deal in highly classified research that has to do with pharmaceuticals, technology, power as well as the human biology coupled with security. The most common form of implementation of biometrics is facial recognition, fingerprints, iris recognition, a retina scanner, and voice recognition into so many applications and scenarios. The integration of this biometrics has had a rising effect and impact of everyday life and has practically changed some daily routines. This paper will examine future integrations of biometrics and it will in time affect everyday life and routine.
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To address the issues like identity theft and security threats, a continuously evolving technology known as biometrics is presently being deployed in a wide range of personal, government, and commercial applications. Despite the great progress in the field, several exigent problems have yet to be addressed to unleash biometrics full potential. This article aims to present an overview of biometric research and more importantly the significant progress that has been attained over the recent years. The paper is envisaged to further not only the understanding of general audiences and policy makers but also interdisciplinary research. Most importantly, this article is intended to complement earlier articles with updates on most recent topics and developments related to e.g. spoofing, evasion, obfuscation, face reconstruction from DNA, Big data issues in biometrics, etc.
Biometric recognition refers to an automatic recognition of individuals based on feature vector(s) derived from their physiological and/or behavioral characteristic. Biometric recognition systems should provide a reliable personal recognition schemes to either confirmer determine the identity of an individual. Applications of such a system include computer systems security, secure electronic banking, mobile phones, credit cards, secure access to buildings, health and social services. By using biometrics a person could be identified basedon "who she/he is" rather than "what she/he has" (card, token, key) or "what she/he knows" (Password, PIN). In this paper, a brief overview of biometric methods, both Unimodal andMultimodal, and their advantages and disadvantages are presented. The term "biometrics" is derived from the Greek words bio (life) and metric (to measure). Biometrics refers to the automatic identification of a person based on his/her physiological or behavioral characteristics. This method of identification is preferred over traditional methods involving passwords and PIN numbers for its accuracy and case sensitiveness. A biometric system is essentially a pattern recognition system which makes a personal identification by determining the authenticity of a specific physiological or behavioral characteristic possessed by the user. An important issue in designing a practical system is to determine how an individual is identified. Depending on the context, a biometric system can be either a verification (authentication) system or an identification system. Verification involves confirming or denying a person's claimed identity while in identification, one has to establish a person's identity. Biometric systems are divided on the basis of the authentication medium used. They are broadly divided as identifications of Hand Geometry, Vein Pattern, Voice Pattern, DNA, Signature Dynamics, Finger Prints, Iris Pattern and Face Detection. These methods are used on the basis of the scope of the testing medium, the accuracy required and speed required. Every medium of authentication has its own advantages and shortcomings. With the increased use of computers as vehicles of information technology, it is necessary to restrict unauthorized access to or fraudulent use of sensitive/personal data. Biometric techniques being potentially able to augment this restriction are enjoying a renewed interest.
A Systematic Review on Physiological-Based Biometric Recognition Systems: Current and Future Trends
Archives of Computational Methods in Engineering, 2021
Biometric deals with the verification and identification of a person based on behavioural and physiological traits. This article presents recent advances in physiological-based biometric multimodalities, where we focused on finger vein, palm vein, fingerprint, face, lips, iris, and retina-based processing methods. The authors also evaluated the architecture, operational mode, and performance metrics of biometric technology. In this article, the authors summarize and study various traditional and deep learning-based physiological-based biometric modalities. An extensive review of biometric steps of multiple modalities by using different levels such as preprocessing, feature extraction, and classification, are presented in detail. Challenges and future trends of existing conventional and deep learning approaches are explained in detail to help the researcher. Moreover, traditional and deep learning methods of various physiological-based biometric systems are roughly analyzed to evaluate them. The comparison result and discussion section of this article indicate that there is still a need to develop a robust physiological-based method to advance and improve the performance of the biometric system.
Computational Intelligence for Biometric Applications: a Survey
International Journal of Computing
Biometric systems consist of devices, procedures, and algorithms used to recognize people based on their physiological or behavioral features, known as biometric traits. Computational intelligence (CI) approaches are widely adopted in establishing identity based on biometrics and also to overcome non-idealities typically present in the samples. Typical areas include sample enhancement, feature extraction, classification, indexing, fusion, normalization, and anti-spoofing. In this context, computational intelligence plays an important role in performing of complex non-linear computations by creating models from the training data. These approaches are based on supervised as well as unsupervised training techniques. This work presents computational intelligence techniques applied to biometrics, from both a theoretical and an application point of view.