Abstract—Face is a complex multidimensional visual model and developing a computational model for face recognition is difficult. The paper presents a methodology for face recognition based on information theory approach of coding and decoding the face image. Proposed methodology is connection of two stages – Feature extraction using principle component analysis and recognition using the feed forward back propagation Neural Network. The algorithm has been tested on 400 images (40 classes). A recognition score for test lot is calculated by considering almost all the variants of feature extraction. The proposed methods were tested on Olivetti and Oracle Research Laboratory (ORL) face database. Test results gave are cognition rate of 97.018%
Index Terms—Face recognition, Principal component analysis (PCA), Artificial Neural network (ANN), Eigenvector, Eigenface.
Mayank Agarwal, Student Member IEEE, Jaypee Institute of Information Technology University, Noida ,India (email: mayank.agarwal@ieee.org).
Nikunj Jain, Student, Jaypee Institute of Information Technology University, Noida, India (email: nikunj262@gmail.com).
Mr. Manish Kumar, Sr. Lecturer (ECE), Jaypee Institute of Information Technology University, Noida, India(email: manish.kumar@jiit.ac.in).
Himanshu Agrawal, Student Member IEEE, Jaypee Institute of Information Technology University, Noida, India (email: himanshuagrawal123@gmail.com).
Cite: Mayank Agarwal, Nikunj Jain, Mr. Manish Kumar and Himanshu Agrawal, "Face Recognition Using Eigen Faces and Artificial Neural Network," International Journal of Computer Theory and Engineering vol. 2, no. 4, pp. 624-629, 2010.
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