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Experimental comparison of approaches for feature extraction of facial attributes

Version 2 2024-06-03, 12:12
Version 1 2016-11-10, 23:28
journal contribution
posted on 2024-06-03, 12:12 authored by A Mohammed, Atul SajjanharAtul Sajjanhar
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.

History

Journal

International journal of computers and applications

Volume

38

Pagination

187-198

Location

Philadelphia, Pa.

ISSN

1206-212X

eISSN

1925-7074

Language

eng

Publication classification

C Journal article, C1 Refereed article in a scholarly journal

Copyright notice

2016, Informa UK

Issue

4

Publisher

Taylor & Francis