Symptoms-Based Biometric Pattern Detection and Recognition

Abstract

AbstractBiometrics play a crucial role in today’s technology world, since it means analyzing biological data. Biometrics is the analysis of the physiology, behavior, or morphology of a person including their face, fingerprint, iris, retina, voice, or signature; a person’s identity can be established through biometrics. In the past decade, biometrics have successfully been applied to areas such as forensic science, security, and identification. In the last three decades, much research has been conducted for the development of biometric systems utilizing fingerprints, voice, iris, and facial recognition, as well as new biometric techniques. Biometric recognition systems must take into account the multiple aspects of processing variable data: obtaining biometric information from a sensor, preprocessing, extraction of features, biometric identification and labeling, verification of labeling, and clustering. Processing images, recognition of patterns, as well as ML (Machine Learning) and artificial intelligence, are used for the analysis of sensor data from a number of perspectives. The aim of the discussion is to provide a summary of the current state of biometric systems and to present techniques for tackling challenges and identifying future research directions. The proposed discussion focuses on evolution of various different types of biometric pattern Detection and recognition approaches, the result and analysis section focuses on two cases: the first section proposes mathematical derivation for enhancement of fingerprint recognition and the second section provides an insight comparison of different approaches to iris recognition. Followed by the section which discusses the latest approaches of biometric recognition approaches in relation to different wearable as well as acquisition devices. The final section gives us an overview of different metrics and their respective accuracies and ability to differentiate individuals based on features obtained from different biometric detection methods.KeywordsBiometricsIdentificationBiometric technologiesBiometric authenticationImage processingRecognition