期刊
PATTERN RECOGNITION
卷 53, 期 -, 页码 102-115出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2015.11.020
关键词
Gabor Wavelets; Feature representation; Face recognition; Spiking neurons; Dimensionality reduction
资金
- Ministry of Higher Education, Malaysia [MIRGS13-02-001-0001]
- Universiti Teknologi MARA, Malaysia
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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