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Nazir, M.; Majid Mirza, A.; Ali Khan, S.
Tipo
Artículo de Investigación
Área del conocimiento
Ingenierías
Título
PSO-GA Based Optimized Feature Selection Using Facial and Clothing Information for Gender Classification
Fecha
2014-02-01
Resumen
Gender classification is a fundamental face analysis task. In previous studies, the focus of most researchers has beenon face images acquired under controlled conditions. Real-world face images contain different illumination effects andvariations in facial expressions and poses, all together make gender classification a more challenging task. In thispaper, we propose an efficient gender classification technique for real-world face images (Labeled faces in the Wild).In this work, we extracted facial local features using local binary pattern (LBP) and then, we fuse these features withclothing features, which enhance the classification accuracy rate remarkably. In the following step, particle swarmoptimization (PSO) and genetic algorithms (GA) are combined to select the most important features" set which moreclearly represent the gender and thus, the data size dimension is reduced. Optimized features are then passed tosupport vector machine (SVM) and thus, classification accuracy rate of 98.3% is obtained. Experiments are performedon real-world face image database.
Tema
Gender Classification; Real-world Face Images; Particle Swarm Optimization; Genetic Algorithm; Local Binary Pattern; Features Fusion.
Idioma
eng
ISSN
ISSN electrónico: 2448-6736; ISSN: 1665-6423
DOI
https://doi.org/10.1016/S1665-6423(14)71614-1
