Research on Bi-mode Biometrics Based on Deep Learning

TL;DR

This paper explores bi-mode biometric identification leveraging deep learning to improve recognition accuracy across various biological features, aiming to enhance security and device authentication.

Contribution

It introduces a deep learning-based approach for bi-mode biometrics, combining multiple biological features for more reliable identification.

Findings

Improved recognition accuracy with bi-mode biometric systems

Enhanced security in public security and device unlocking

Potential for long-term development in biometric applications

Abstract

In view of the fact that biological characteristics have excellent independent distinguishing characteristics,biometric identification technology involves almost all the relevant areas of human distinction. Fingerprints, iris, face, voice-print and other biological features have been widely used in the public security departments to detect detection, mobile equipment unlock, target tracking and other fields. With the use of electronic devices more and more widely and the frequency is getting higher and higher. Only the Biometrics identification technology with excellent recognition rate can guarantee the long-term development of these fields.