Vlsi implementation of multimodal biometric recognition using sclera and fingerprint based on anfis and ga

Abstract

Authentication is the process of ensuring and confirming a person s identity and is generally implemented by login credentials. The use of single factor knowledge based authentication system such as username and password is insufficient for protecting against authentication attacks. In future, single biometric framework might not be in a position to accomplish the wanted execution prerequisite in genuine world provisions. To overcome these issues, we have to utilize multimodal biometric confirmation frameworks which blend data from various modalities to make a choice. Multimodal biometric confirmation framework utilizes more than one human modalities namely face, iris, retina, sclera and fingerprint etc., so as to improve the security standards. In this research, the combined biometric features of sclera and fingerprint images for addressing a person s identification were discussed that was not implemented earlier. This work includes the sclera and fingerprint feature extraction based on our proposed research work. The main objective of this thesis is to analyze the performance of fingerprint and sclera images using our proposed algorithms EFMAGANFIS - Enhanced Fingerprint Matching Algorithm using Genetic Adaptive Neuro Fuzzy Inference System. 2. ESMAGANFIS - Enhanced Sclera Matching Algorithm using Genetic Adaptive Neuro Fuzzy Inference System. 3. EMFSMAGANFIS - Enhanced Multimodal Fingerprint Sclera Matching Algorithm using Genetic Adaptive Neuro Fuzzy Inference System newline