A SURVEY ON MACHINE AND DEEP LEARNING FOR FINGER KNUCKLE PRINT AUTHENTICATION
Sathiya Lakshmanan, Palanisamy Velliyan, Abdelouahab Attıa · 2023
Beneficially, biometric personal authentication is very important for the research community due to its broad applicability to several security applications. In terms of precision and computational complexity, manual biometric frameworks are considered to be more fruitful. In hand-based biometrics, finger knuckle prints are one of the biometric surface features that can be used to ensure personal authentication. The result is the fact that the external surface of the finger back knuckle region shows fluctuating and exclusive patterns. The existing analysis of FKP authentication system such as Geometrical, statistical, transform and texture-based methods are usually a time-consuming and difficult task. With advanced machine learning techniques and profound learning concepts, it can be made easy and fast and can be very useful for society. So, the essential place of this paper is to consider a survey of the different machine learning and deep learning-based Finger Knuckle Print verification system. This survey will assist aspiring researchers in the area of finger knuckle print authentication systems.