Gender classification based on fuzzy clustering and principal component analysis. Issue 3 (1st April 2016)

• Record Type: Journal Article
• Authors: Hassanpour, Hamid; Zehtabian, Amin; Nazari, Avishan; Dehghan, Hossein
• Is Part Of: IET computer vision. Volume 10:Issue 3(2016)
• Page Start: 228
• Page End: 233
• Publication Date: 2016-04-01
• Subjects: face recognition -- feature extraction -- image classification -- gender issues -- pattern clustering -- data reduction -- fuzzy set theory -- principal component analysis; gender classification method -- fuzzy clustering technique -- principal component analysis -- computer vision -- facial gender detection -- frontal facial images -- PCA -- feature extraction step -- feature classification step -- data dimensionality reduction -- computational time -- FG-Net database -- Stanford database -- FERET database; Computer vision -- Periodicals; Pattern recognition systems -- Periodicals; 006.37
• DOI: 10.1049/iet-cvi.2015.0041
• Languages: English
• ISSNs: 1751-9632

Abstract

Abstract : Gender classification is one of the most challenging problems in computer vision. Facial gender detection of neonates and children is also known as a highly demanding issue for human observers. This study proposes a novel gender classification method using frontal facial images of people. The proposed approach employs principal component analysis (PCA) and fuzzy clustering technique, respectively, for feature extraction and classification steps. In other words, PCA is applied to extract the most appropriate features from images as well as reducing the dimensionality of data. The extracted features are then used to assign the new images to appropriate classes – male or female – based on fuzzy clustering. The computational time and accuracy of the proposed method are examined together and the prominence of the proposed approach compared to most of the other well‐known competing methods is proved, especially for younger faces. Experimental results indicate the considerable classification accuracies which have been acquired for FG‐Net, Stanford and FERET databases. Meanwhile, since the proposed algorithm is relatively straightforward, its computational time is reasonable and often less than the other state‐of‐the‐art gender classification methods.