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A comparative study of 14 state of art descriptors for face recognition

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Abstract

This research paper presents a comparative study between 14 state of art descriptors which includes Local Binary Pattern (LBP), Median Binary Pattern (MBP), 6 × 6 Multiscale Block LBP (6 × 6 MB-LBP), Local Neighborhood Difference Pattern (LNDP), Logically Connected-LBP (LC-LBP), Local Phase Quantization (LPQ), Compound LBP (CLBP), Horizontal Elliptical LBP (HELBP), Vertical Elliptical LBP (VELBP), ELBP, Neighborhood Intensity Based LBP (NI-LBP), Median Robust Extended LBP Based on NI (MRELBP-NI), Radial Difference-LBP (RD-LBP) and Transition LBP (tLBP). For all the descriptors the features are extracted globally and the dimensionality of the feature size is reduced by employing Principal Component Analysis (PCA) and Fishers Linear Discriminant Analysis (FLDA). Finally classification is performed by Support Vector Machines (SVMs) and Nearest Neighbor (NN). Experiments are performed on 8 challenging databases which covers all the major challenges such as pose variations, illumination variations, expression variations and occlusion changes. The 8 challenging databases includes ORL, GT, Faces94, MIT-CBCL, Yale, YB, EYB and SOF. Out of all the descriptors it is the performance of the CLBP descriptor which is most encouraging. On some occasions the MRELBP-NI descriptor also achieves good results. But all in all the CLBP descriptor achieves the best results. In addition to this Deep learning based descriptors are also discussed in the paper.

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Acknowledgements

The author would like to thank Massachusetts Institute of Technology (MIT) and Center for Biological and Computational Learning (CBCL) for providing the facial image database. The database rights are completely reserved by the MIT.

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  1. Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, Uttarakhand, India

    Shekhar Karanwal

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Karanwal, S. A comparative study of 14 state of art descriptors for face recognition. Multimed Tools Appl 80, 12195–12234 (2021). https://doi.org/10.1007/s11042-020-09833-2

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