Face recognition using principle component analysis, eigenface and neural network

Authors:
Mayank Agarwal;Nikunj Jain;Manish Kumar;Himanshu Agrawal
Affiliations:
Jaypee Institute of Information Technology University, Noida ,India;Jaypee Institute of Information Technology University, Noida ,India;Jaypee Institute of Information Technology University, Noida ,India;Jaypee Institute of Information Technology University, Noida ,India
Venue:
SENSIG'09/VIS'09/MATERIALS'09 Proceedings of the 2nd WSEAS International Conference on Sensors, and Signals and Visualization, Imaging and Simulation and Materials Science
Year:
2009

Citing 2
Cited 1

Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces

IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfaces for recognition

Journal of Cognitive Neuroscience

Emotional human-machine interaction: cues from facial expressions

HI'11 Proceedings of the 2011 international conference on Human interface and the management of information - Volume Part I

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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 a recognition rate of 97.018%.