Recognizing faces with normalized local Gabor features and Spiking Neuron Patterns
Abstract
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.
- Publication:
-
Pattern Recognition
- Pub Date:
- May 2016
- DOI:
- 10.1016/j.patcog.2015.11.020
- Bibcode:
- 2016PatRe..53..102K
- Keywords:
-
- Gabor Wavelets;
- Feature representation;
- Face recognition;
- Spiking neurons;
- Dimensionality reduction