Quality-based pattern C2 code score-level fusion in multimodal biometric authentication system using pattern net

Biometrics-based authentication is a most needed activity in a corporate and business world. Genuineness, accuracy and reliability are the most common characteristics of any authentication system. This requires any multimodal unique biometric traits combined with better fusion strategy. This paper have proposed C-2-based fusion algorithm for combining the two complicated, unique, minute detailed finger vein and iris images for reduced feature vector extraction. A reduced feature vectors from this C-2 algorithm is given to the neural net known as pattern net for pattern matching. The combined strategy is applied for 250 data sets for verification and gives better performance in R-2 value, equal error rate, false acceptance rate, etc.