3 August 2015 Blur and occlusion invariant palmprint recognition with block-wise local phase quantization histogram
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Abstract
A method for palmprint verification and identification, which is robust to blurred and occluded palmprints is proposed. An algorithm is proposed for region of interest (ROI) extraction that aligns each palmprint ROI on the same position. First-level decomposition of the ROI using a Haar wavelet gives an approximation ROI (AROI) and reduces computational overhead. Local phase quantization (LPQ) based on quantization of phase information by local Fourier transform is applied on AROI to obtain blur invariant palmprint features. The LPQ image is divided into M×M nonoverlapping blocks, and histograms of these blocks are considered as features. The entropy of each block is used to identify it as occluded or nonoccluded. The average chi-square distance between corresponding nonoccluded blocks is calculated for measuring similarity. Experiments performed on PolyU 2D, CASIA, IITD, and IIITDMJ palmprint databases show that the proposed system is independent of acquisition device. Moreover, to justify the robustness of the proposed system, experiments are performed with variable amounts of blur and occlusion present in test palmprints. The performance of the proposed system is compared with several local feature-based palmprint recognition techniques on normal as well as occluded palmprints.
© 2015 SPIE and IS&T
Deepti Tamrakar, Pritee Khanna, "Blur and occlusion invariant palmprint recognition with block-wise local phase quantization histogram," Journal of Electronic Imaging 24(4), 043006 (3 August 2015). https://doi.org/10.1117/1.JEI.24.4.043006 . Submission:
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