Multimodal Biometric in Computer Vision
Sunayana Kundan Shivthare, Yogesh Kumar Sharma, Ranjit D. Patil · 2023
In conjunction with the growing requirement for security regulations and information security worldwide, biometric technology is more prevalent daily than ever. Multimodal biometrics technology has gained popularity due to overcoming several significant drawbacks of unimodal biometric systems. Using numerous biometric markers by personal identification systems to identify individuals is multimodal biometrics. Unlike unimodal biometrics, which uses only one biometric feature, such as a fingerprint, face, palm print, or iris, multimodal authentication is more secure different biometrics systems aid in confirming that only authentic users are using the services. Using cutting-edge approaches like ML, computer vision, object detection and recognition, image analysis pattern recognition, and CNN is the general idea behind biometric identification methods. Machine learning and deep learning are widespread fields in today's digital era. While surfing the Internet, algorithms for machine learning and deep learning are used in every aspect of the online world. This shows that these fields have become an inseparable part of our lives. Abundance data produced through online mediums are classified through these techniques. In computer vision, these algorithms have prominently left their footprints. Deep learning is a subset of machine learning that studies and applies artificial neural networks (ANNs). Deep learning is at the heart of modern artificial intelligence, and its applications rapidly spread across industries and domains. In this chapter, the authors have tried to illuminate applications of machine learning and deep learning concepts and algorithms in connection with multimodal biometrics.