An evolutionary wrapper for feature selection in face recognition applications

Abstract

Active shape models is an adaptive shape-matching technique that has been used for locating facial features in images. However, when a number of features is extracted for each landmark point, distortions caused by noise or illumination, and the dimensionality of the final representation, have a negative impact in the performance of a classifier. In this paper, an evolutionary wrapper for selection of the most relevant set of features for face recognition is presented. The proposed strategy explores the space of multiple feasible selections using genetic algorithms. Experimental results show that the proposed approach allows to improve the classification performance in comparison with another enhanced method and a state of the art face recognition approach.

A Study of Feature Subset Selection Methods for Dimension Reduction

“…As a result of weighting the features according on the way to a process base the performance of ASM was improved on adjusted mutual information. Author in this paper [8] present a wrapper meant for choosing the majority pertinent features for face identification. Author used a Genetic Algorithm GA to discover the space of sufficient solution, guide through the accurateness obtain within a face categorization job.…”

Swarm intelligence and evolutionary computation approaches for 2D face recognition: a systematic review

“…Vignolo et al (2012) proposed the use of GA to select the features extracted by means of Active Shape Model (ASM). The search is guided by a classi er so that the classi cation success rate is assigned as tness value for each evaluated individual.…”

Feature selection for face recognition based on multi-objective evolutionary wrappers

“…In Behaine and Scharcanski (2012), the authors proposed to im- (Chatterjee and Bhattacherjee, 2011;Hsu et al, 2011;Li et al, 92 2010; Pedrycz and Ahmad, 2012), and for the search of optimal 93 representations (Charbuillet et al, 2009;Vignolo et al, 2011a,b; 94 Vignolo et al, 2013). In Vignolo et al (2012) Objective Genetic Algorithm (Coello Coello et al, 2007). This…”

“…In this work, the face detector proposed by Demirel and Anbarjafari (2009) is used, and the process applied to the database of face images in order to obtain the ASM-based set of features is described in detail in Scharcanski (2012), Vignolo et al (2012).…”