Evolution of Deep Learning for Biometric Identification and Recognition

Evolution of Deep Learning for Biometric Identification and Recognition

S. Miruna Joe Amali, Manjula Devi C., Rajeswari G.
Copyright: © 2023 |Pages: 14
DOI: 10.4018/978-1-7998-8892-5.ch009
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Abstract

Biometrics is a method based on the recognition of the biological characteristics of an individual like fingerprint, vocal, and facial features. Biometric features hold a unique place when it comes to recognition, authentication, and security applications as they cannot be easily duplicated. Deep learning-based models have been very successful in achieving better efficiency in biometric recognition. They are more beneficial because deep learning-based models provide an end-to-end learning framework.
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Deep Learning Models

Deep learning models in general are prepared based on an objective function, yet the way in which the objective function is planned uncovers a ton about the reason for the model.

Deep Learning in Computer Vision

Computer vision is the technological advancement which exemplifies interpretation capability of machines with respect to images and videos. Computer vision algorithms extract specific features and criteria in images and videos for analysis purpose. They then use the extracted features for interpretations, predictions or for decision making. In recent years deep learning techniques have gained prominence in the application of computer vision. In particular, Convolutional Neural Networks (CNNs) architecture is prominently used. CNN is a multi-layered architecture that enables a neural network to focus on the most relevant features in the image. CNNs have been successfully applied to various fields relating to computer vision like object recognition, face recognition, scene identification etc.

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