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Eigenvectors of Covariance Matrix using Row Mean and Column Mean Sequences for Face Recognition

Author(s): Dr.H.B.Kekre, Sudeep D. Thepade, Akshay Maloo

Journal: International Journal of Biometric and Bioinformatics
ISSN 1985-2347

Volume: 4;
Issue: 2;
Start page: 42;
Date: 2010;
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Keywords: Face recognition | eigenvectors | covariance matrix | row mean | column mean.

Face recognition has been a fast growing, challenging and interesting area in real-timeapplications. A large number of face recognition algorithms have been developed from decades.Principal Component Analysis (PCA) [2][3] is one of the most successful techniques that hasbeen used in face recognition. Four criteria for image pixel selection to create feature vector wereanalyzed: the first one has all the pixels considered by converting the image into gray plane, thesecond one is based on taking row mean in RGB plane of face image, the third one is based ontaking column mean in RGB plane finally, the fourth criterion is based on taking row and columnmean of face image in RGB plane and feature vector were generated to apply PCA technique.Experimental tests on the ORL Face Database [1] achieved 99.60% of recognition accuracy, withlower computational cost. To test the ruggedness of proposed techniques, they are tested on ourown created face database where 80.60% of recognition accuracy is achieved.For a 128 x 128 image that means that one must compute a 16384 x 16384 matrix and calculate16,384 eigenfaces. Computationally, this is not very efficient as most of those eigenfaces are notuseful for our task. Using row mean and column mean reduces computations resulting in fasterface recognition with nearly the same accuracy.

Tango Jona
Tangokurs Rapperswil-Jona

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