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

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 applyi...

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Detalles Bibliográficos
Autor principal: Uçar, Ayşegül
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3914600/
https://www.ncbi.nlm.nih.gov/pubmed/24558319
http://dx.doi.org/10.1155/2014/628494
Descripción
Sumario: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, YSCr, Z (n) SCr, and B (n) SCr, are firstly constructed using the Cb and Cr component images of the Y Cb Cr color space, the S color component of the HSV color spaces, and the Z (n) and B (n) color components of the normalized XYZ 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.