Gender classification via gradientfaces

Date

2010-11-12

Author

Loǧoǧlu, K. Berker
Saracoǧlu, Ahmet
Esen, Ersin
Alatan, Abdullah Aydın

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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In this paper illumination invariant, pose and facial expression tolerant gender classification method is proposed. A recently introduced feature extraction method, namely Gradientfaces, is utilized together with Support Vector Machine (SVM) as a classifier. Image regions obtained from cascaded Adaboost based face detector is used at the feature extraction step and faster classification is achieved by using only 20-by-20 pixel region during feature extraction. For performance evaluation, two well-known face databases, FERET and Yale B are tested and the algorithm is compared against a pixelbased algorithm on these datasets. The results indicate that Gradientfaces significantly outperform the pixel-based methods under severe illumination, pose and facial expression variances. © 2011 Springer Science+Business Media B.V.

Subject Keywords

Gender Classification, Gradientface, Support Vector Machine

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=78651524858&origin=inward
https://hdl.handle.net/11511/106886

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Department of Electrical and Electronics Engineering, Conference / Seminar