Recognizing faces with normalized local Gabor features and Spiking Neuron Patterns
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 empl...
| Main Authors: | , |
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| Format: | Article |
| Language: | English English |
| Published: |
Elsevier
2016
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/49551/ http://irep.iium.edu.my/49551/ http://irep.iium.edu.my/49551/ http://irep.iium.edu.my/49551/1/1-s2.0-S0031320315004409-main.pdf http://irep.iium.edu.my/49551/4/49551_Recognizing%20faces_wos_scopus.pdf |
| Summary: | 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. |
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