Dependence structure of Gabor wavelets based on copula for face recognition. (15th December 2019)

• Record Type: Journal Article
• Authors: Li, Chaorong; Huang, Yuanyuan; Xue, Yu
• Is Part Of: Expert systems with applications. Volume 137(2019)
• Page Start: 453
• Page End: 470
• Publication Date: 2019-12-15
• Subjects: Face recognition -- Gabor wavelets -- Gaussian copula -- Covariance matrix -- Particle swarm optimization; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2019.05.034
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Face recognition methods are proposed on the dependence structure of Gabor wavelets. We develop lightweight copula model of Gabor wavelets to extract the face features. PSO is use to improve the performance of lightweight copula model of Gabor wavelets. Abstract: Low resolution, difficult illumination and noise are the important factors that affect the performance of face recognition system. In order to counteract these adverse factors, in this paper we propose copula probability models based on Gabor wavelets for face recognition. Gabor wavelets have robust performance under lighting and noise conditions. The strong dependencies exist in the domain of Gabor wavelets due to their non-orthogonal property. In the light of the structure characteristic of Gabor wavelet subbands, the proposed methods use copula to capture the dependencies to represent the face image. Three probability-model-based methods CF-GW (Copula Function of Gabor Wavelets), LCM-GW (Lightweight Copula Model of Gabor Wavelets) and LCM-GW-PSO (Lightweight Copula Model of Gabor Wavelets with Particle Swarm Optimization) are proposed for face recognition. Experiments of face recognition show our proposed methods are more robust under the conditions of low resolution, lighting and noise than the popular methods such as the LBP-based methods and other Gabor-based methods. The face features extracted by our methods belong to the Riemannian manifold which is different to Euclidean space. In order to deal the issue of face recognition in complex environment, we can combine the face features in Riemannian manifold with the face features in Euclidean space to obtain the more robust face recognition system by using expert system technologies such as reasoning model and multi-classifier fusion.