Research on Bi-mode Biometrics Based on Deep Learning
This work targets biometric identification for applications such as security and device unlocking, but appears incremental as it builds on existing deep learning methods without introducing a new paradigm.
The paper addresses the need for high-accuracy biometric identification in fields like public security and mobile devices, proposing a bi-mode biometrics approach using deep learning to improve recognition rates, though no specific numerical results are provided.
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.