Hybrid Multimodal Biometric Recognition using Kekre's Wavelets, 1D Transforms & Kekre's Vector Quantization Algorithms Based Feature Extraction of Face & Iris

Face Recognition Systems are becoming ubiquitous and inevitable in today’s world. Being less intrusive and universal face recognition systems serve as good option for access control and surveillance. Iris recognition enjoys universality, high degree of uniqueness and moderate user co-operation. This makes Iris recognition systems unavoidable in emerging security & authentication mechanisms. Various unimodal system implement face & iris based biometric authentication are available. In this paper a multimodal system which is a combination of a unimodal face recognition and multi-algorithmic iris recognition is proposed, the proposed system is combination of unimodal face and multi-algorithmic iris recognition system hence the name hybrid multimodal. Multimodal biometric systems are becoming more and more popular, they have more accuracy as compared to unimodal biometric systems. On the other hand these systems are more complex. Face features are extracted using multilevel decomposition of face image using a new family of wavelet called kekre’s wavelet and the iris features are extracted using 1 D transform of row & column mean, kekre’s wavelet based texture features and Kekre’s Fast Codebook Generation (KFCG) & Kekre’s Median Codebook Generation (KMCG) algorithms based VQ codebooks. The proposed system gives 99% correct classification rate (CCR) as compared to 85% CCR of face recognition system and 96% CCR of iris recognition system. General Terms Pattern Recognition, Security, Algorithms, Biometrics.