Abstract

The Automatic Face Recognition system is widely applied in new technologies. This system works beyond the ability of human vision. The limited vision of human eye in identifying vast number of human faces is overcome by the automatic face recognition with many more advantages. The basic purpose of face recognition system is to compare the image video which is stored in a database with the image video in real time variation. Many techniques have been used in face recognition system. This paper present a survey of several techniques used in face recognition system, an approach to the detection and identification of human face.

References

• A. J. Goldstein, L. D Harmon and A. B Lesk "Identification of human Face," Proc . IEEE, May 1971, vol. 59, No. 5, pp. 748-760.
• Pandya, Jigar M. , Devang Rathod, and Jigna J. Jadav. "A Survey of Face Recognition approach. " International Journal of Engineering Research and Applications (IJERA) 3. 1 (2013): 632-635.
• M. Kirby, L. Sirovich, Application of the Karhunen–Loève procedure for the characterization of human face, IEEE transactions on pattern analysis and machine intelligence. vol. 12, January 1990.
• M. A. Turk, A. P. Pentland, Face recognition using eigen faces, in: Proceedings of the IEEE Conference on CVPR, 1991, pp. 586–591.
• Lu, J. , Plataniotis Kostantinos, N. , Venetsanopoulos Anastasios, N. , 2003. Face recognition using LDA-based algorithms. IEEE Trans. Neural Networks Vol. 14, No (1), pp. 195–200.
• P. N. Belhumeur, J. P. Hespanha, D. J. Kriegman, Eigen faces vs. Fisher faces: recognition using class specific linear projection, IEEE Transactions On Pattern Analysis and Machine Intelligence, Vol. 19, No. 7, July 1997,pp. 711–720.
• Vinoth, M. , et al. "Recognition Based System Using Principal Component Linear Discriminant Analysis. " methods 2. 1 (2012).
• Dinesh Kumar , C. S. Rai and Shakti Kumar, ' Face Recognition Using Self-Organizing Map and Principal Component Analysis' 2005 IEEE,pp. 1469-1473.
• S. -H. Lin, S. -Y. Kung, L. -J. Lin, Face recognition/detection by probabilistic decision-based neural network, IEEE Trans. Neural NetworkVol. 8, No. (1) (1997), pp. 114–132.
• Lawrence, Richard D. , George S. Almasi, and Holly E. Rushmeier. "A scalable parallel algorithm for self-organizing maps with applications to sparse data mining problems. " Data Mining and Knowledge Discovery 3. 2 (1999): 171-195.
• Ghorpade, Santaji, Jayshree Ghorpade, and Shamla Mantri,Patter Recognition using Neural Networks, International Journal of Computer Science and Technology (IJCST) 2. 6 (2010 )pp. 92-98.
• A. Pentland, B. Moghaddam, T. Starner, View-based and modular eigenspaces for face recognition, in: Proceedings of the IEEE Conference on CVPR, 1994, pp. 84–91.
• Y. Gao, M. K. H. Leung, Face recognition using line edge map, IEEE Transactions On Pattern Analysis And Machine Intelligence, Vol. 24, No. 6, June (2002),pp. 764–779.
• R. Brunelli, T. Poggio, Face recognition: features versus templates, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 15, No. 10, October (1993), pp. 1042–1052.
• R. Gottmukkal and V. K. Asari, "An improved face recognition technique based on modular PCA approach," Pattern Recognition Letters, Vol. 24, No. 4, pp. 429-436, 2004.
• M. Lades, J. C. Vorbruggen, J. Buhmann, J. Lange, C. Vander Malsburg, R. P. Wurtz, W. Konen, Distortion invariant object recognition in the dynamic link architecture, IEEE Trans. Comput. Vol. 42 (1993), PP. 300–311.
• D. Aradhana, H. Girish, K. Karibasappa and C. Reddy, Face recognition by elastic bunch graph method using a group based adaptive tolerant neural network, Vol. 1, No. (4) (2001), pp. 194–203.
• H. C. Shin, J. H. Park, S. D. Kim, Combination of warping robust elastic graph matching and kernel-based projection discriminate analysis for face recognition, IEEE Trans. Multimedia 9 (6) (2007),pp. 1125–1136.
• T. Ahonen, A. Hadid, M. Pietikainen, Face description with local binary patterns: application to face recognition IEEE Transactions On Pattern Analysis And Machine Intelligence, Vol. 28, No. 12, December 2006,pp. 2037–2041.
• Gao, Yongsheng, and Yutao Qi. "Robust visual similarity retrieval in single model faces databases. " Pattern Recognition 38. 7 (2005), pp. 1009-1020.
• T. Ojala, M. Pietikainen, T. Maenpaa, Multi resolution gray-scale and rotation invariant texture classification with local binary patterns, IEEE Trans. Pattern Anal. Mach. Intell. vol. 24,No (7) (2002) ,pp. 971–987.
• Huang, Di, et al. "Local binary patterns and its application to facial image analysis: a survey. " Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on 41. 6 (2011): 765-781.