Eigenfaces Technique, an Improved Face Recognition Approach Using Neural Network
Author : Mohd Ashraf and Md. Zair HussainVolume 8 No.2 April-June 2019 pp 98-104
Abstract
Image analysis and understanding, stands tall amongst all the technologies and face recognition is an eminent part of it. A face database is maintained as a logbook to identify an input face. This is accomplished by mere comparison amongst the face database. There are several face recognition techniques, of which, symmetry, Elastic Bunch Graph Matching (EBGM), and analytic-to-holistic recognition have been explored in this research paper. Other peculiar approaches like image based face recognition techniques like MLP, convolutional neural network, eigenfaces, associative neural networks, recirculation neural network and independent component analysis have been thoroughly discussed. Two vibrant face recognition databases, UMIST and ORL have proved to be extremely important in analyzing the results of face recognition. Eigen Face value approach has been anticipated with the associated analysis of results of face recognition. Another approach in face recognition is optimized multiperceptron, which will be acting as the reference to the optimized eigenfaces approach in this research paper, hence making this study more efficient through comparison.
Keywords
Biometry, Face Recognition, Principle Component Analysis, ORL Face Database, Multi-Layer Perceptron
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References
[1] OnsenToygar and Adnan Acan, “Face Recognition using Auto associative Neural Networks: An experimental evaluation on the FERET database”, In Proc. Of International XII Turkish symposium on artificial intelligence and neural networks, 2003.
[2] D. Reisfeld and Y. Yeshurun, “Robust Detection of Facial Features by Generalized Symmetry”, In Proc. Of International Conference on Pattern Recognition, pp. 117- 120, 1992
[3] Kim-Man Lam and Hong Yan, “An Analytic-to-Holistic Approach for Face Recognition Based on a single Frontal view”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 20, No. 7, pp. 673-686, July 1998.
[4] Laurenz Wiskott, Jean-Marc Fellous, Norbert Kruger and Christoph von der Malsburg, “Face Recognition by Elastic Bunch Graph Matching”, IEEE Transactions On Pattern Analysis And Machine Intelligence, Vol. 19, No. 7, pp. 775- 779, July 1997.
[5] Dmitry Bryliuk and Valery Starovoitov, “Access control by face recognition using neural networks and negative examples”,In Proc. of The 2nd International Conference on Artificial Intelligence, Crimea, Ukraine, pp. 428-436, Sept.16-20, 2002.
[6] Z. Pan, A. G.Rust and H. Bolouri, “Image Redundancy Reduction for Neural Network Classification using Discrete Cosine Transforms”, In Proc. of the International Joint Conference on Neural Networks, Vol. 3, pp. 149-154, 2000.
[7] S. Lawrence, C.L. Giles, A.C. Tsoi and A.D. Back, “Face Recognition: A Convolutional Neural Network Approach”, IEEE
Transactions on Neural Networks, Special Issue on Neural Networks and Pattern Recognition, Vol. 8, No. 1, pp. 98-113, 1997.
[8] M. Turk and A. Pentland, “Eigenfaces for recognition”, Journal of Cognitive Neuroscience, Vol. 3, pp. 72-86, 1991.
[9] Dmitry Bryliuk and Valery Starovoitov, “Application of recirculation neural network and principal component analysis for face recognition”, In Proc. of the 2nd International Conference on Neural Networks and Artificial Intelligence, Minsk, Belarus, pp. 136-142, Oct. 2-5, 2001.
[10] Marian Stewart Bartlett, Javier R. Movellan and Terrence J. Sejnowski, “Face Recognition by Independent Component Analysis”, IEEE transactions on Neural Networks, Vol. 13, No. 6, pp. 1450-1464, Nov. 2002.
[11] Fu Jie Huang, Zhihua Zhou, Hong-Jiang Zhang and Tsuhan Chen, “Pose Invariant Face Recognition”, In Proc. of the 4th IEEE International Conference on Automatic Face and Gesture Recognition, Grenoble, France, pp. 245-250, 2000.
[12] UMIST Face Database, [Online] Available at: http://images.ee.umist. .ac.uk/danny/database.html
[13] ORL Face Database [Online] Available at: www.c1.cam.ac.uk/resea rch/DTG/ attarchive: pub/data a/att_faces.tar.Z
[14] S. Eickeler, S. Muller, and G. Rigoll, “High performance face recognition using Pseudo 2-D Hidden Markov Models”, In Proc. of European Control Conference (ECC),Aug.1999
[15] M. Ashraf and Rashid Ali, “Design of an Optimal Multilayer Neural Network for Eigenfaces Based Face Recognition,” In the Proc. of IEEE sponsored International Conference on Recent Applications of Soft Computing in Engineering and Technology (RASIET-07), Alwar, India, pp. 253-263, 2007.