Neural Network Based Approach for Face Detection cum Face Recognition

Authors: Kesari Verma, Aniruddha S. Thoke, Pritam Singh

Abstract:

Automatic face detection is a complex problem in image processing. Many methods exist to solve this problem such as template matching, Fisher Linear Discriminate, Neural Networks, SVM, and MRC. Success has been achieved with each method to varying degrees and complexities. In proposed algorithm we used upright, frontal faces for single gray scale images with decent resolution and under good lighting condition. In the field of face recognition technique the single face is matched with single face from the training dataset. The author proposed a neural network based face detection algorithm from the photographs as well as if any test data appears it check from the online scanned training dataset. Experimental result shows that the algorithm detected up to 95% accuracy for any image.

Keywords: Face Detection, Face Recognition, NN Approach, PCA Algorithm.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1333596

References:

[1] Craw, D. Tock, and A. Bennett, "Finding face features," Proc. of 2nd European Conf. Computer Vision. pp. 92-96, 1992.
[2] A. Lanitis, C. J. Taylor, and T. F. Cootes, "An automatic face identification system using flexible appearance models," Image and Vision Computing, vol.13, no.5, pp.393-401, 1995.
[3] T. K. Leung, M. C. Burl, and P. Perona, "Finding faces in cluttered scenes using random labeled graph matching," Proc. 5th IEEE int-l Conf. Computer Vision, pp. 637-644, 1995.
[4] B. Moghaddam and A. Pentland, "Probabilistic visual learning for object recognition," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no.7. pp. 696-710, July, 1997.
[5] M. Turk and A. Pentland, "Eigenfaces for recognition," J. of Cognitive Neuroscience, vol.3, no. 1, pp. 71-86, 1991.
[6] M. Kirby and L. Sirovich, "Application of the Karhunen-Loeve procedure for the characterization of human faces," IEEE Trans. Pattern Analysis and Machine Intelligence, vol.12, no.1, pp. 103-108, Jan. 1990.
[7] I. T. Jolliffe, Principal component analysis, New York: Springer-Verlag, 1986.
[8] T, Agui, Y. Kokubo, H. Nagashi, and T. Nagao,"Extraction of face recognition from monochromatic photographs using neural networks," Proc. 2nd Int-l Conf. Automation, Robotics, and Computer Vision, vol.1, pp. 18.81-18.8.5, 1992.
[9] Krestinin, I.A., Seredin, O.S.: Excluding cascading classifier for face detection. Proc. of the 19th Int. Conf. on Computer Graphics and Vision, 380-381 (2009).
[10] Kienzle, W., Bakir, G., Franz, M., Scholkopf, B.: Face detection - efficient and rank deficient, Advan. in neural inform. process. systems 17, 2005. - P. 673-680 (2005).
[11] Nikolay Degtyarev and Oleg Seredin, Tula State University. Comparitive testing of face detection algorithm The original publication is available at www.springerlink.com.
[12] Jesorsky, O., Kirchberg, K.J., Frischholz, R.W.: Robust face detection using the hausdorff distance, Lecture Notes in Computer Science, June, 90-95 (2001).
[13] M. H. Yang and N. Ahuja. Detecting huamn faces in color images. IEEE Proc. of Int. Conf. on Image Proc. (ICIP -98), 1998.
[14] Nilamani Bhoi, Mihir Narayan Mohanty Matching based Eye Detection in Facial Image. International Journal of Computer Applications (0975 - 8887)Volume 12- No.5, December 2010.
[15] http://www.facedetectioncode.com
[16] T. Sakai, M. Nagao, and T. Kanade, Computer analysis and classification of photographs of human faces, in Proc. First USAÔÇöJapan Computer Conference, 1972, p. 2.7.
[17] I. Craw, H. Ellis, and J. R. Lishman, Automatic extraction of facefeature, Pattern Recog. Lett. Feb. 1987, 183-187.
[18] L. C. De Silva, K. Aizawa, and M. Hatori, Detection and tracking of facial features by using a facial feature model and deformable circular template, IEICE Trans. Inform. Systems E78-D(9), 1995, 1195-1207.
[19] V. Govindaraju, Locating human faces in photographs, Int. J. Comput. Vision 19, 1996.
[20] R. Herpers, H. Kattner, H. Rodax, and G. Sommer, G aze: An attentive processing strategy to detect and analyze the prominent facial regions, in IEEE Proc. of Int. Workshop on Automatic Face- and Gesture- Recognition, Zurich, Switzerland, Jun. 1995, pp. 214-220.
[21] P. J. L. Van Beek, M. J. T. Reinders, B. Sankur, and J. C. A. Van Der Lubbe, Semantic segmentation of videophone image sequences, in Proc. of SPIE Int. Conf. on Visual Communications and Image Processing, 1992, pp. 1182-1193.
[22] H. P. Graf, E. Cosatto, D. Gibson, E. Petajan, and M. Kocheisen, Multimodal system for locating heads and faces, in IEEE Proc. of 2nd Int. Conf. on Automatic Face and Gesture Recognition, Vermont, Oct. 1996, pp. 277-282.
[23] K. M. Lam and H. Yan, Facial feature location and extraction for computerised human face recognition, in Int. Symposium on information Theory and Its Applications, Sydney, Australia, Nov. 1994.
[24] P. J. L. Van Beek, M. J. T. Reinders, B. Sankur, and J. C. A. Van Der Lubbe, Semantic segmentation of
[25] videophone image sequences, in Proc. of SPIE Int. Conf. on Visual Communications and Image Processing,1992, pp. 1182-1193.
[26] J. L. Crowley and F. Berard, Multi-model tracking of faces for video communications, in IEEE Proc. of Int,Conf. on Computer Vision and Pattern Recognition, Puerto Rico, Jun. 1997.
[27] H. P. Graf, T. Chen, E. Petajan, and E. Cosatto, Locating faces and facial parts, in IEEE Proc. of Int.Workshop on Automatic Face-and Gesture-Recognition, Zurich, Switzerland, Jun. 1995, pp. 41-45.