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Title |
Face Recognition in compressed domain by applying Canonical Correlation Analysis based feature vector optimization and Neural Network matching |
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Author |
Menila James, Arockiaswamy |
| Citation |
Vol. 12 No. 5 pp. 77-84 |
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Abstract |
This research work presents an efficient approach for performing face recognition in compressed domain by applying Canonical Correlation Analysis based feature vector optimization. CCA is a dominant method for multivariate analysis and therefore a powerful method of feature projection based on CCA is proposed for compressed facial images. A major advantage of the proposed approach is the fact that face recognition systems can straightly work with JPEG2000 compressed images as it uses entropy points as input to the new face recognition system based on CCA. Cascade Forward back propagation neural network is used in the new method for matching of the images. Labeling of images is applied in the lateral part of the research for better recognition of images with varying expressions. The experimental results proved that the proposed method is very effective in achieving high Recognition Rate (RR) and Normalized Recognition Rate (NRR) with great reduction of computational time. |
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Keywords |
Image compression, Discrete Wavelet Transform, Face Recognition, Canonical Correlation Analysis, Neural Networks |
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