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Recognizing faces prone to occlusions and common variations using optimal face subgraphs
B.M. Lahasan, I. Venkat, , S.L. Lutfi, P.D. Wilde
Published in Elsevier Inc.
2016
Volume: 283
   
Pages: 316 - 332
Abstract
An intuitive graph optimization face recognition approach called Harmony Search Oriented-EBGM (HSO-EBGM) inspired by the classical Elastic Bunch Graph Matching (EBGM) graphical model is proposed in this contribution. In the proposed HSO-EBGM, a recent evolutionary approach called harmony search optimization is tailored to automatically determine optimal facial landmarks. A novel notion of face subgraphs have been formulated with the aid of these automated landmarks that maximizes the similarity entailed by the subgraphs. For experimental evaluation, two sets of de facto databases (i.e., AR and Face Recognition Grand Challenge (FRGC) ver2.0) are used to validate and analyze the behavior of the proposed HSO-EBGM in terms of number of subgraphs, varying occlusion sizes, face images under controlled/ideal conditions, realistic partial occlusions, expression variations and varying illumination conditions. For a number of experiments, results justify that the HSO-EBGM shows improved recognition performance when compared to recent state-of-the-art face recognition approaches. © 2016 Elsevier Inc.
About the journal
JournalData powered by TypesetApplied Mathematics and Computation
PublisherData powered by TypesetElsevier Inc.
ISSN00963003