Recognizing faces with normalized local Gabor features and Spiking Neuron Patterns

PATTERN RECOGNITION

卷:53

Article

Kamaruzaman, Fadhlan¹  Shafie, Amir Akramin² 

[1] Univ Teknol MARA, Dept Comp Engn, Shah Alam 40450, Selangor, Malaysia

[2] Int Islamic Univ Malaysia, Dept Mechatron Engn, POB 10, Kuala Lumpur 50728, Malaysia

关键词: Gabor Wavelets;    Feature representation;    Face recognition;    Spiking neurons;    Dimensionality reduction;   

DOI  :  10.1016/j.patcog.2015.11.020

来源: Elsevier

【 摘 要 】

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. (C) 2015 Elsevier Ltd. All rights reserved.

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