Pattern Recognition Using Context Dependent Memory Model (CDMM) in Multimodal Authentication System

Pattern recognition is one of the prime concepts in current technologies in both private and public sectors. The analysis and recognition of two or more patterns is a complex task due to several factors. The consideration of two or more patterns requires huge space for keeping the storage media as well as computational aspect. Vector logic gives very good strategy for recognition of patterns. This paper proposes pattern recognition in multimodal authentication system with the use of vector logic and makes the computation model hard and less error rate. Using PCA two to three biometric patterns will be fusion and then various key sizes will be extracted using LU factorization approach. The selected keys will be combined using vector logic, which introduces a memory model often called Context Dependent Memory Model (CDMM) as computational model in multimodal authentication system that gives very accurate and very effective outcome for authentication as well as verification. In the verification step, Mean Square Error (MSE) and Normalized Correlation (NC) as metrics to minimize the error rate for the proposed model and the performance analysis will be presented.