Methodological approach for Face Recognition Using Artificial Neural Networks

International Journal of Computer Trends and Technology (IJCTT)          
© - May Issue 2013 by IJCTT Journal
Volume-4 Issue-5                           
Year of Publication : 2013
Authors :K. Shirisha, S. Vijaya Lakshmi, N. Musrat Sultana


K. Shirisha, S. Vijaya Lakshmi, N. Musrat Sultana "Methodological approach for Face Recognition Using Artificial Neural Networks"International Journal of Computer Trends and Technology (IJCTT),V4(5):1164-1170 May Issue 2013 .ISSN Published by Seventh Sense Research Group.

Abstract: -Automatic Facial Feature Detection is becoming a very important task in applications such as Model Based Video coding, Facial Image Animation, Face Recognition, Facial Emotion Recognition, Intelligent Human Computer Interaction. Most of the approaches for facial feature detection have been proposed which use independent facial feature detectors relying on hand designed filters that aim at segmenting using image properties such as edges, intensity, color, motion. In this paper, A Hierarchical neural based facial feature detection scheme is proposed for robustly and automatically detect a set of user-selected facial features in images that is designed to precisely locate fine features in faces of variable size and appearance.. The proposed system comprises two successive stages: Generalized Rapid Transformation (GRT) and Normalization.


[1] A. M. Martinez and A. C. Kak, “PCA versus LDA”, IEEE transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 2, pp. 228-233, 2001.
[2] Pattern Recognition and Image Analysis by Earl Gose, Richard Johnson baugh and Steve Jost.
[3] Yuzuko Utsumi Yoshio Iwai and Hiroshi Ishiguro, “Face Tracking and Recognition Considering the Camera's Field of View", pp. 52-63.
[4] Nefian A. V., Monson, “Hidden Markov Model for Face Recognition,” IEEE International Conference on Acoustics, Speech and Signal Processing(ICASSp98), Seatle (1998)2721-2724.
[5] Jun Wang Zhang Yi, Jacek M. Zurada Bao-Liang Lu Hujun Yin, “Advances in Neural Network", Third International Symposium on Neural Networks, May/June 2006.
[6] Adnan Khashman, “Face Recognition using Neural Networks and Pattern Averaging” , Dept of EEE, Near East University, Turkey.
[7] Belhumeur P. Hespanha, J. Kreigman, D. J.: Eigenfaces Vs Fisherfaces: “Recognition using Class Specific Linear Projection", IEEE Transactions on Pattern Analysis and Machine Intelligence, 711-720(1997).
[8] Osuna E., Freund . R., Girosit , F, “Training Support Vector Machines: An Application to face detection", Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 130-136,1997.
[9] Ying-li Tian, Takeo Kanade, Jeffrey F. Cohn, "Recognizing action units for facial expression analysis", Pattern Analysis and Machine Intelligence.
[10] Brunelli, R. & T. Poggio, "Face Recognition: Features versus Templates", IEEE Transactions on PAMI, 15(10):1042-1052,1993.

Keywords — Face Recognition, Facial Feature Detection, Face Detection, Coarse Feature Detection, Fine Feature Detection.