Face recognition has been an important and hard problem in computer vision application. Recently, haar cascade method has proven to be an effective method for face recognition. In this thesis, a simple and powerful approach is developed for real time multi face recognition system. A threefold stage application is developed. in the first stage face detection is done by using Haar cascade. In second stage, Haar cascade features are stored in the memory and compare with the data base faces. In third stage the matched database face is recognized for the query face and displayed with recognized face name. The three stages are mentioned as face detection, feature extraction storage & comparison and at last face recognition. The experimental results for real time multiple recognition system is shown and elaborates the benefit of using the described approach.

Face detection, Face recognition, Feature extraction, Haar cascade.

[1] Y.-F. Yao, X.-Y.Jing, and H.-S. Wong, "Face and palmprint feature level fusion for single sample biometrics recognition," Neurocomputing, Vol.70, pp. 1582-1586, 2007.
[2] J. Zhou, G. Su, C. Jiang, Y. Deng, and C. Li, "A faceand fingerprint identity authentication system based on multi-route detection," Neurocomputing, Vol.70, pp.922-931, 2007.
[3] C. Nastar and M. Mitschke, "Real time face recognition using feature combination," in ThirdIEEE International Conference on Automatic Faceand Gesture Recognition. Nara, Japan, 1998, pp. 312-317.
[4] S. Gong, S. J. McKenna, and A. Psarrou, “Dynamic Vision: From Images to Face Recognition” ImperialCollege Press (World Scientific Publishing Company),
[5] B. Moghaddam and M. H. Yang, "Learning Gender with Support Faces," IEEE Transactions on PatternAnalysis and Machine Intelligence, Vol.24, pp.707-711, 2002.
[6] J. N. K. Liu, M. Wang, and B. Feng, "iBotGuard: an Internet-based intelligent robot security system using invariant face recognition against intruder," IEEETransactions on Systems Man And Cybernetics PartC-Applications And Reviews, Vol.35, pp.97-105, 2005.
[7] R. Brunelli and T. Poggio, "HyperBF Networks for Gender Classification," Proceedings of DARPAImage Understanding Workshop, pp.311-314, 1992.
[8] A. Colmenarez, B. J. Frey, and T. S. Huang, "A probabilistic framework for embedded face and facial expression recognition," in Proceedings of theIEEE Conference on Computer Vision and PatternRecognition, Vol.1. Ft. Collins, CO, USA, 1999, pp. 1592-1597.

[9] Y. Shinohara and N. Otsu, "Facial Expression Recognition Using Fisher Weight Maps," in SixthIEEE International Conference on Automatic Faceand Gesture Recognition, Vol.100, 2004, pp.499-504.
[10] F. Bourel, C. C. Chibelushi, and A. A. Low, "Robust Facial Feature Tracking," in British Machine VisionConference.Bristol, 2000, pp.232-241.
[11] K. Morik, P. Brockhausen, and T. Joachims, "Combining statistical learning with a knowledgebasedapproach -- A case study in intensive care monitoring," in 16th International Conference onMachine Learning (ICML-99). San Francisco, CA, USA: Morgan Kaufmann, 1999, pp.268-277.
[12] S. Singh and N. Papanikolopoulos, "Vision-based detection of driver fatigue," Department of Computer Science, University of Minnesota, Technical report 1997.
[13] D. N. Metaxas, S. Venkataraman, and C. Vogler, "Image-Based Stress Recognition Using a Model- Based Dynamic Face Tracking System," InternationalConference on Computational Science, pp.813-821, 2004.
[14] M. M. Rahman, R. Hartley, and S. Ishikawa, "A Passive AndMultimodal Biometric System for Personal Identification," in International Conferenceon Visualization, Imaging and Image Processing. Spain, 2005, pp.89-92.