View-Invariant Face Detection for Colorful Image

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Abstract:

To overcome illumination changes and pose variations, a pose-invariant face detection method is presented. First, an illumination compensation method based on reference white is presented to overcome the lighting variations. The reference white is obtained according to the component Y from YCbCr color space. Then, a mixture face model is constructed by the Cb and Cr from YCbCr color space and H from the HSV color space to extract faces from colorful image. At last, an eyes model is designed to locate eyes in the obtained face images, which can distinguish face from neck and arms ultimately. The presented method is conducted on the CASIA face database. The experimental results have shown that our method is robust to pose changes and illumination variations, and it can achieve well performance.

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Advanced Materials Research (Volumes 945-949)

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1880-1884

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June 2014

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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[1] B. Li, L. Shen and Y. Wang: A novel eye location algorithm based on radial symmetry transform, In: Proceedings of 18th International Conference on Pattern Recognition, Vol. 3(2006), pp.511-514.

DOI: 10.1109/icpr.2006.136

Google Scholar

[2] A. Opel, M. P. A. Fussenegger and P. Auer: Weak hypotheses and boosting for generic object detection and recognition, In: Proc. Internet. European Conf. on Computer Vision (ECCV), Vol. 3021 (2004), pp.402-413.

DOI: 10.1007/978-3-540-24671-8_6

Google Scholar

[3] D. Zhang, S. Z. Li and D. Gatica-Perez: Real-time face detection using boosting in hierarchical feature spaces, In: Proceedings of the International Conference on Pattern Recognition, Vol. 2(2004), pp.411-414.

DOI: 10.1109/icpr.2004.1334238

Google Scholar

[4] H. A. Rowley, S. Baluja and T. Kanade: IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 20 (1998), pp.23-38.

DOI: 10.1109/34.655647

Google Scholar

[5] E. Osuna, R. Freund and F. Girosi, Training support vector machines: An application to face detection, In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (1997), pp.130-136.

DOI: 10.1109/cvpr.1997.609310

Google Scholar

[6] P. Viola and M. J. Jones: Rapid object detection using a boosted cascade of simple features, In: Proc. Internet. Conf. on Computer Vision and Pattern Recognition (CVPR), Vol. 1(2001), pp.511-518.

DOI: 10.1109/cvpr.2001.990517

Google Scholar

[7] Y. J. Chen and Y. C. Lin: Simple face-detection algorithm based on minimum facial features, the 33rd Annual Conference of the IEEE Industrial Electronics Society (2007), pp.455-460.

DOI: 10.1109/iecon.2007.4460051

Google Scholar

[8] PH. Lee, SW. Wu and YP. Hung: IEEE Transaction on Image Processing, Vol. 21(2012), pp.4280-4289.

Google Scholar

[9] S. Choi: Int J Adv Robotic Sy, Vol. 9 (2012), pp.130-137.

Google Scholar

[10] W. Zheng and Y. Dai: Computer Engineering and Applications (In Chinese), 47(13) (2011), pp.195-198.

Google Scholar

[11] Information on http: /biometrics. idealtest. org.

Google Scholar