Recognizing faces with normalized local Gabor features and Spiking Neuron Patterns

Kamaruzaman, Fadhlan and Shafie, Amir Akramin (2016) Recognizing faces with normalized local Gabor features and Spiking Neuron Patterns. Pattern Recognition, 53. 102 - 115. ISSN 0031-3203

	PDF - Published Version Restricted to Repository staff only Download (3MB) | Request a copy
	PDF Restricted to Repository staff only Download (108kB) | Request a copy

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.

Item Type:	Article (Journal)
Additional Information:	5119/49551
Uncontrolled Keywords:	Dimensionality reduction
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering > Department of Mechatronics Engineering
Depositing User:	Dr Amir Shafie
Date Deposited:	22 Mar 2016 11:01
Last Modified:	20 Dec 2016 16:48
URI:	http://irep.iium.edu.my/id/eprint/49551

Actions (login required)

View Item