alexa Abstract | A MATLAB based Face Recognition using PCA with Back Propagation Neural network
ISSN ONLINE(2320-9801) PRINT (2320-9798)

International Journal of Innovative Research in Computer and Communication Engineering
Open Access

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Research Article Open Access


Automatic recognition of people is a challenging problem which has received much attention during recent years due to its many applications in different fields. Face recognition is one of those challenging problems and up to date, there is no technique that provides a robust solution to all situations. There are many techniques used for this purpose. Face recognition is an effective means of authenticating a person. In this paper, a face recognition system for personal identification and verification using Principal Component Analysis (PCA) with Back Propagation Neural Networks (BPNN) is proposed. The dimensionality of face image is reduced by the PCA and the recognition is done by the BPNN for face recognition. The system consists of a database of a set of facial patterns for each individual. The characteristic features of pca called „eigenfaces‟ are extracted from the stored images, which is combine with Back Propagation Neural Network for subsequent recognition of new images.

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Author(s): Priyanka Dhoke, M.P. Parsai


Neural Networks, Principal Component Analysis, Eigen Values, Eigen Vector, Back Propagation Neural Network, Neural Network

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