{"title":"Face Localization and Recognition in Varied Expressions and Illumination","authors":"Hui-Yu Huang, Shih-Hang Hsu","volume":65,"journal":"International Journal of Computer and Information Engineering","pagesStart":715,"pagesEnd":721,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/5662","abstract":"
In this paper, we propose a robust scheme to work face alignment and recognition under various influences. For face representation, illumination influence and variable expressions are the important factors, especially the accuracy of facial localization and face recognition. In order to solve those of factors, we propose a robust approach to overcome these problems. This approach consists of two phases. One phase is preprocessed for face images by means of the proposed illumination normalization method. The location of facial features can fit more efficient and fast based on the proposed image blending. On the other hand, based on template matching, we further improve the active shape models (called as IASM) to locate the face shape more precise which can gain the recognized rate in the next phase. The other phase is to process feature extraction by using principal component analysis and face recognition by using support vector machine classifiers. The results show that this proposed method can obtain good facial localization and face recognition with varied illumination and local distortion.<\/p>\r\n","references":"[1] A. S. Georghiades, D. J. Kriegman, and P. N. Belhumeur, \"From few to\r\nmany: illumination cone models for face recognition under variable lighting and pose,\" IEEE Trans. Pattern Anal. Mach. Intel., vol. 23, no. 6, pp. 643-660, 2001.\r\n[2] RM Makwana, \"Illumination invariant face recognition: A survey of\r\npassive methods,\" Procedia Computer Science, vol. 2, pp. 101-110,2010.\r\n[3] S. Marcel and J. Keomany, \"Robust-to-illumination face localization using active shape models and local binary patterns,\" IDIAP Research Report, pp. 6-47, 2006.\r\n[4] X. Ling, Y. Wang, Z. Zhang, and Y. Wang, \"On-line signature verification\r\nbased on Gabor features,\" in Proc. of the 19th Annual Wireless and Optical Communications, 2010, pp. 1-4.\r\n[5] H. R. Hsu, Apply an adaptive center selection algorithm to radial basis\r\nfunction committee machine for face recognition, Master Thesis, National\r\nYunlin University of Science and Technology, 2008.\r\n[6] V. Rankov, R. J. Locke, R. J. Edens, P. R. Barber, and B. Vojnovic, \"An\r\nalgorithm for image stitching and blending,\" in Proc. of SPIE, no. 5701,\r\n2005, pp. 190-199.\r\n[7] R. Kohavi, \"A study of cross-validation and bootstrap for accuracy\r\nestimation and model selection,\" in Proc. of Fourteenth Int. Joint Conf.\r\non Artificial Intelligence, pp. 1137-1143, 1995.\r\n[8] M. J. Lyons, S. Akamasku, M. Kamachi, and J. Gyoba, \"Coding facial\r\nexpressions with Gabor wavelets,\" in Proc. of Int. Conf. on Automatic\r\nFace and Gesture Recognition, pp. 200-205, 1998.\r\n[9] P. Viola and M. Jones, \"Robust real-time object detection,\" International\r\nJournal of Computer Vision, vol. 57, no. 2, pp. 137-154, 2004.\r\n[10] T. Fawcett, \"An introduction to ROC analysis,\" Pattern Recognition\r\nLetters, vol.27, no. 8, pp. 861-874, 2006.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 65, 2012"}