An Analytic Gabor Feedforward Network for Single-Sample and Pose-Invariant Face Recognition

Beom Seok Oh, Kar Ann Toh, Andrew Beng Jin Teoh, Zhiping Lin

Research output: Contribution to journalArticle

16 Citations (Scopus)

Abstract

Gabor magnitude is known to be among the most discriminative representations for face images due to its space- frequency co-localization property. However, such property causes adverse effects even when the images are acquired under moderate head pose variations. To address this pose sensitivity issue and other moderate imaging variations, we propose an analytic Gabor feedforward network which can absorb such moderate changes. Essentially, the network works directly on the raw face images and produces directionally projected Gabor magnitude features at the hidden layer. Subsequently, several sets of magnitude features obtained from various orientations and scales are fused at the output layer for final classification decision. The network model is analytically trained using a single sample per identity. The obtained solution is globally optimal with respect to the classification total error rate. Our empirical experiments conducted on five face data sets (six subsets) from the public domain show encouraging results in terms of identification accuracy and computational efficiency.

Original languageEnglish
Pages (from-to)2791-2805
Number of pages15
JournalIEEE Transactions on Image Processing
Volume27
Issue number6
DOIs
Publication statusPublished - 2018 Jun

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Graphics and Computer-Aided Design

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