Journal
EXPERT SYSTEMS WITH APPLICATIONS
Volume 108, Issue -, Pages 170-182Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2018.04.039
Keywords
Statistical pattern recognition; Unconstrained face recognition; Maximum likelihood estimation; CNN (Convolution neural network); Kullback-Leibler divergence; Off-the-shelf deep features
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Funding
- Russian Federation President grant [MD-306.2017.9]
- RSF grant [14-41-00039]
- Russian Science Foundation [17-41-00002, 14-41-00039] Funding Source: Russian Science Foundation
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The paper deals with unconstrained face recognition task for the small sample size problem based on computation of distances between high-dimensional off-the-shelf features extracted by deep convolution neural network. We present the novel statistical recognition method, which maximizes the likelihood (joint probabilistic density) of the distances to all reference images from the gallery set. This likelihood is estimated with the known asymptotically normal distribution of the Kullback-Leibler discrimination between nonnegative features. Our approach penalizes the individuals if their feature vectors do not behave like the features of observed image in the space of dissimilarities of the gallery images. We provide the experimental study with the LFW (Labeled Faces in the Wild), YTF (YouTube Faces) and IJB-A (IARPA Janus Benchmark A) datasets and the state-of-the-art deep learning-based feature extractors (VGG-Face, VGGFace2, ResFace-101, CenterFace and Light CNN). It is demonstrated, that the proposed approach can be applied with traditional distances in order to increase accuracy in 0.3-5.5% when compared to known methods, especially if the training and testing images are significantly different. (C) 2018 Elsevier Ltd. All rights reserved.
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