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The Scientific World Journal
Volume 2014 (2014), Article ID 628494, 15 pages
http://dx.doi.org/10.1155/2014/628494
Research Article

Color Face Recognition Based on Steerable Pyramid Transform and Extreme Learning Machines

Mechatronics Engineering Department, Engineering Faculty, Firat University, 23119 Elazig, Turkey

Received 12 August 2013; Accepted 7 October 2013; Published 16 January 2014

Academic Editors: S. Bourennane and J. Marot

Copyright © 2014 Ayşegül Uçar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper presents a novel color face recognition algorithm by means of fusing color and local information. The proposed algorithm fuses the multiple features derived from different color spaces. Multiorientation and multiscale information relating to the color face features are extracted by applying Steerable Pyramid Transform (SPT) to the local face regions. In this paper, the new three hybrid color spaces, , , and , are firstly constructed using the and component images of the color space, the color component of the color spaces, and the and color components of the normalized color space. Secondly, the color component face images are partitioned into the local patches. Thirdly, SPT is applied to local face regions and some statistical features are extracted. Fourthly, all features are fused according to decision fusion frame and the combinations of Extreme Learning Machines classifiers are applied to achieve color face recognition with fast and high correctness. The experiments show that the proposed Local Color Steerable Pyramid Transform (LCSPT) face recognition algorithm improves seriously face recognition performance by using the new color spaces compared to the conventional and some hybrid ones. Furthermore, it achieves faster recognition compared with state-of-the-art studies.