Novel image fusion scheme based on dependency measure for robust multispectral palmprint recognition

• Published in: Pattern recognition Vol. 47; no. 6; pp. 2205 - 2221
• Main Authors: Raghavendra, R., Busch, Christoph
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.06.2014; Elsevier
• Subjects: Applied sciences; Biometrics; Classification; Computer vision; Confidence intervals; Dependency test; Detection, estimation, filtering, equalization, prediction; Exact sciences and technology; Feature extraction; Image fusion; Image processing; Information, signal and communications theory; Multispectral palmprints; Mutual information; Pattern recognition; Recognition; Signal and communications theory; Signal processing; Signal representation. Spectral analysis; Signal, noise; Telecommunications and information theory; Wavelet transforms; Biometrics; Multispectral palmprints; Dependency test; Image fusion; Mutual information; Automatic classification; State of the art; Spectral band; Image processing; Gabor transformation; Database; Sparse representation; Gabor filter; Discriminant analysis; Measurement sensor; Pattern recognition; Signal classification; Kernel method; Statistical method; Interest region; Wavelet transformation; Signal processing; Feature extraction; Automatic recognition; Palm print
• ISSN: 0031-3203; 1873-5142
• DOI: 10.1016/j.patcog.2013.12.011

Multispectral palmprint is considered as an effective biometric modality to accurately recognize a subject with high confidence. This paper presents a novel multispectral palmprint recognition system consisting of three functional blocks namely: (1) novel technique to extract Region of Interest (ROI) from the hand images acquired using a contact less sensor (2) novel image fusion scheme based on dependency measure (3) new scheme for feature extraction and classification. The proposed ROI extraction scheme is based on locating the valley regions between fingers irrespective of the hand pose. We then propose a novel image fusion scheme that combines information from different spectral bands using a Wavelet transform from various sub-bands. We then perform the statistical dependency analysis between these sub-bands to perform fusion either by selection or by weighted fusion. To effectively process the information from the fused image, we perform feature extraction using Log-Gabor transform whose feature dimension is reduced using Kernel Discriminant Analysis (KDA) before performing the classification by employing a Sparse Representation Classifier (SRC). Extensive experiments are carried out on a CASIA multispectral palmprint database that shows the strong superiority of our proposed fusion scheme when benchmarked with contemporary state-of-the-art image fusion schemes. •Novel adaptive weighted image fusion scheme to combine different multi-spectral palmprints.•Dynamic switching between selection and weighted fusion to achieve high accuracy.•Novel Scheme for palmprint ROI extraction based on hand shape.•Extensive qualitative and quantitative analysis of the proposed (ROI & fusion) scheme.•Benchmarking with well known state-of-the-art schemes that include 5 different contemporary multi-spectral image fusion schemes and 2 well known state-of-the-art palmprint recognition schemes.