Experimental comparison of approaches for feature extraction of facial attributes

Mohammed, Ahmed Abdulateef and Sajjanhar, Atul 2016, Experimental comparison of approaches for feature extraction of facial attributes, International journal of computers and applications, vol. 38, no. 4, pp. 187-198, doi: 10.1080/1206212X.2016.1207427.

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Title Experimental comparison of approaches for feature extraction of facial attributes
Author(s) Mohammed, Ahmed AbdulateefORCID iD for Mohammed, Ahmed Abdulateef orcid.org/0000-0002-3026-095X
Sajjanhar, AtulORCID iD for Sajjanhar, Atul orcid.org/0000-0002-0445-0573
Journal name International journal of computers and applications
Volume number 38
Issue number 4
Start page 187
End page 198
Total pages 12
Publisher Taylor & Francis
Place of publication Philadelphia, Pa.
Publication date 2016
ISSN 1206-212X
Keyword(s) facial attributes classification
demographic information
expression information
Summary In this paper, we compare the effectiveness of widely used approaches for representation of facial features in face images. Feature extraction is performed on face images for representation of four facial attributes, namely gender, age, race, and expression, by using discrete wavelet transform (DWT), Gabor wavelet, scale-invariant feature transform, local binary pattern (LBP), and Eigenfaces. After feature extraction and dimension reduction, demographic and expression classification is performed to identify the most discriminating techniques for representation of facial features. Extensive experiments are performed using publicly available face databases, namely Yale, Face95 Essex, and Cohn-Kanade (CK+) databases. Experimental results show that DWT, LBP, and Gabor wavelet methods are robust to variations of illumination, facial expression, and geometric transformations. Experimental results also show that race and expression are more difficult to predict than gender and age.
Language eng
DOI 10.1080/1206212X.2016.1207427
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2016, Informa UK
Persistent URL http://hdl.handle.net/10536/DRO/DU:30088723

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