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Low and high frequency wavelet sub-band-based feature extraction

In a biometric system, feature extraction is an important task for faster and efficient identification of a person. A new feature extraction method, sub-band PCA+LDA is proposed to extract distinct features from low frequency and high frequency wavelet sub-bands. The proposed method captures both local and global features of two biometrics under consideration, face and iris. The matching scores of face and iris are normalised using minmax and tanh techniques, and fused using sum rule, product rule and weighted sum rule. For unimodal systems, the proposed method gives better recognition rate in comparison to other existing methods, like DWT, DWT+PCA, DWT+LDA, local binary pattern and subspace LDA. The performance of the proposed multimodal biometric system is superior to unimodal system in terms of attaining maximum of 100% recognition rate and minimum equal error rate (EER) of 0.017 for standard biometric databases.