Recognizing faces with normalized local Gabor features and Spiking Neuron Patterns

• Published in: Pattern Recognition
• Main Author: Kamaruzaman F.; Shafie A.A.
• Format: Article
• Language: English
• Published: Elsevier Ltd 2016
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84958103321&doi=10.1016%2fj.patcog.2015.11.020&partnerID=40&md5=00aad2753c3bdd4df69e7540f9ed0bae

Gabor Wavelets (GW) have been extensively used for facial feature representation due to its inherent multi-resolution and multi-orientation characteristics. In this work we extend the work on Local Gabor Feature Vector (LGFV) and propose a new face recognition method called LGFV//LN//SNP, which employs local normalization filter in pre-processing stage. We propose a novel Spiking Neuron Patterns (SNP) as a dimensionality reduction method to reduce the dimensions of local Gabor features. SNP is acquired from projection of LGFV//LN features using Spike Response Model (SRM), a neuron model describing the spike behavior of a biological neuron. Results on AR, FERET, Yale B and FRGC 2.0 face datasets showed that SNP implementation delivered significant improvement in accuracy. Comparisons with several previously published results also suggested that LGFV//LN//SNP achieved better results in some tests. Additionally, LGFV//LN//SNP requires relatively smaller number of GW than LGFV//LN to produce optimal results. © 2015 Elsevier Ltd. All rights reserved.