Iris feature extraction through wavelet mel-frequency cepstrum coefficients. (February 2019)

• Record Type: Journal Article
• Authors: Barpanda, Soubhagya Sankar; Majhi, Banshidhar; Sa, Panjak Kumar; Sangaiah, Arun Kumar; Bakshi, Sambit
• Is Part Of: Optics & laser technology. Volume 110(2019)
• Page Start: 13
• Page End: 23
• Publication Date: 2019-02
• Subjects: AUC Area Under Curve -- CDF Cohen-Daubechies-Feauveau -- CHT Circular Hough Transform -- DCT Discrete Cosine Transformation -- DFT Discrete Fourier Transformation -- DWT Discrete Wavelet Transform -- EER Equal Error Rate -- FAR False Acceptance Rate -- FFT Fast Fourier Transformation -- FPC Fourier Phase Code -- FRR False Rejection Rate -- FV Feature Vector -- GAR Genuine Acceptance Rate -- HOG Histograms of Oriented Gradients -- IDFT Inverse Discrete Fourier Transformation -- IDO Integro-Differential Operator -- JPEG Joint Photographic Expert Group -- LBP Local Binary Patterns -- LoG Laplacian of Gaussian -- MFC Mel-Frequency Cepstrum -- MFCC Mel-Frequency Cepstrum Coefficients -- MLDF Multi-lob Differential Filters -- MT Mel Transformation -- NIR Near Infra Red -- POC Phase-Only Correlation -- PILP Phase Intensive Local Pattern -- ROC Receiver Operating Characteristic -- SIFT Scale Invariant Feature Transform -- THFB Triplet Half-band Filter Babk -- VS Visual Spectrum -- WCF Wavelet Cepstrum Feature -- WT Wavelet Transformation; Wavelet cepstrum feature -- Iris segmentation -- Iris recognition; Optics -- Periodicals; Lasers -- Periodicals; Electronic journals; 621.366
• DOI: 10.1016/j.optlastec.2018.03.002
• Languages: English
• ISSNs: 0030-3992

Abstract

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.