Contemporary Biometric System Design

Contemporary Biometric System Design

Himanshu Purohit (Sir Padampat Singhania University, India) and Pawan K. Ajmera (Birla Institute of Technology and Science, Pilani, India)
DOI: 10.4018/978-1-7998-2772-6.ch016
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Individual's Identity Authentication depends on physical traits like face, iris, and fingerprint, etc., or behavioral traits like voice and signature. With the rapid advancement in the field of biometrics, multimodal biometric systems are replacing unimodal biometric systems. As the application of molecular biometric system removes certain errors like noisy data, interclass variations, spoof attacks, and unacceptable error rates as compared to unimodal biometric systems. Even the possibilities of multiple scenarios present in multimodal biometric systems are quite helpful for the consolidation of information using different levels of fusion. In this chapter, the authors try to analyze the technological change which is present due to growing field of biometrics with artificial intelligence and undergone a thorough research for multimodal biometric systems for effective authentication purpose. This study is quite helpful for getting different perception for the use of biometrics as a highest level of network security due to the fusion of many different modalities.
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This chapter basically provides a learning knowledge about the latest research and development in the field of biometrics and the chapter discusses about the major issues like:

  • Literature survey of the various aspects of biometrics.

  • Categorization of biometric process and relevant applications.

  • Providing a brief outline for mainstream biometric authentication technologies.

  • List of top class companies working in the field of biometrics.

  • Clarifying the important technical aspects involved in biometrics

  • Important selection parameters of biometric traits

  • Classification of biometric systems

  • Concept of Biometric Fusion

  • Discussion on performance parameters of biometric system

  • Specific advantage of multimodal biometrics

  • Covering Artificial Intelligence with Biometrics

  • Discussion on AI enabled Biometric system

  • Brief classification of Neural Networks for biometrics

  • Future of biometrics in AI era

  • Application of AI in healthcare biometric world

  • Legal aspects regarding privacy protection

  • Outline of future research directions and conclusion.

Key Terms in this Chapter

Fusion: It is the use of multiple types of biometric data or ways of processing the data to improve the performance of biometric systems.

Failure To Enroll (FTE): It is the percentage of population which fails to complete enrollment for a biometric solution or application.

Unimodal Biometrics: It refers to the use of a single biometric trait of the individual for identification and verification purpose.

Convolutional Neural Network (CNN): These are neural networks used primarily to classify images, cluster images by similarity and perform object recognition within scenes.

Facial Recognition: It is a biometric software application capable of uniquely identifying or verifying a person by comparing and analyzing patterns based on the person’s facial contours.

False Match Rate (FMR): It is the rate at which a biometric process mismatches biometric signals from two distinct individuals as coming from the same individual.

False Non-Match Rate (FNMR): It is the probability that the system fails to detect a match between the input pattern and a matching template in the database.

Failure To Acquire (FTA): It is used to describe a biometric system’s failure to extract usable identification data from a biometric sample.

Multimodal Biometrics: It refers to the use of a combination of two or more biometric modalities in an identification system.

Artificial Neural Networks (ANN): These are the pieces of computing system designed to simulate the way the human brain analyzes and process information.

Artificial Intelligence (AI): It refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions.

Biometric Modality: It is a category of a biometric system depending upon the type of human trait it takes as input.

Internet of Things (IoT): It is a system of interrelated computing devices, mechanical and digital machines, objects, animals, or people that are provided with unique identifiers and the ability to transfer data over a network without requiring human to human or human to computer interaction.

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