NDLI: Gender classification based on fuzzy clustering and principal component analysis

Content Provider	IET Digital Library
Author	Hassanpour, Hamid Zehtabian, Amin Nazari, Avishan Dehghan, Hossein
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.
Starting Page	228
Ending Page	233
Page Count	6
ISSN	17519632
Volume Number	10
e-ISSN	17519640
Issue Number	Issue 3, Apr (2016)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/10/3
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2015.0041
Journal	IET Computer Vision
Publisher Date	2016-04-01
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Combinatorial Mathematics Computational Time Computer Vision Computer Vision And Image Processing Technique Data Dimensionality Reduction Data Handling Technique Data Reduction Face Recognition Facial Gender Detection Feature Classification Step Feature Extraction Feature Extraction Step FERET Database FG-Net Database Frontal Facial Image Fuzzy Clustering Technique Fuzzy Set Theory Gender Classification Method Gender Issues Image Classification Image Recognition Pattern Clustering PCA Principal Component Analysis Stanford Database Statistics
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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