Local gradient-based illumination invariant face recognition using local phase quantisation and multi-resolution local binary pattern fusion

Soodeh Nikan; Majid Ahmadi

Local gradient-based illumination invariant face recognition using local phase quantisation and multi-resolution local binary pattern fusion

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Author(s): Soodeh Nikan ¹ and Majid Ahmadi ¹
- Affiliations: 1: Department of Electrical and Computer Engineering, University of Windsor, Windsor, Ontario N9B 3P4, Canada
Source: Volume 9, Issue 1, January 2015, p. 12 – 21
DOI: 10.1049/iet-ipr.2013.0792 , Print ISSN 1751-9659, Online ISSN 1751-9667

Received 20/11/2013, Accepted 07/06/2014, Revised 17/05/2014, Published 16/07/2014

A local-based illumination insensitive face recognition algorithm is proposed which is the combination of image normalisation and illumination invariant descriptors. Illumination insensitive representation of image is obtained based on the ratio of gradient amplitude to the original image intensity and partitioned into smaller sub-blocks. Local phase quantisation and multi-scale local binary pattern, extract the sub-regions characteristics. Distance measurements of local nearest neighbour classifiers are fused at the score level to find the best match and decision-level fusion combines the results of two matching techniques. Entropy, class posterior probability and mutual information are utilised as the weights of fusion components. Simulation results on the YaleB, Extended YaleB, AR, Multi-PIE and FRGC databases show the improved performance of the proposed algorithm under severe illumination with low computational complexity and no reconstruction or training requirement.

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Local gradient-based illumination invariant face recognition using local phase quantisation and multi-resolution local binary pattern fusion

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