Iris feature extraction through wavelet mel-frequency cepstrum coefficients. (February 2019) - Barpanda, Soubhagya Sankar; Majhi, Banshidhar; Sa, Panjak Kumar; Sangaiah, Arun Kumar; Bakshi, Sambit
Highlights: A wavelet cepstrum based feature extraction technique is proposed for iris recognition. The technique helps in emphasizing the high frequency components for achieving better recognition accuracy. The wavelet cepstrum feature helps in dimensionality reduction of the feature set. The validity of the concept is verified by detailed experiments. Abstract: In this paper, a novel technique based on wavelet cepstrum feature is discussed for iris recognition system. The proposed method is based on the wavelet derived from the popular biorthogonal Cohen-Daubechies-Feauveau 9/7 filter bank. Moreover, being biorthogonal in nature it has superior frequency selectivity, symmetric, and better time-frequency localization. The suggested scheme deals with computing the two level detail coefficients from the normalized iris template. Then these detailed coefficients are then divided into non-uniform bins in a logarithmic manner. This helps in reducing the dimension of the wavelet coefficients followed by assigning non-uniform weights to the different frequency components. Then the discrete cosine transform of the same is computed, from which the energy feature is extracted. The proposed technique is experimentally validated with publicly available databases: CASIAv3, UBIRISv1, and IITD. The performance of the proposed approach is found be superior to that of the state-of-the-art methods.