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A bidirectional two-dimensional principal component analysis (2DPCA) is proposed for human face recognition using curvelet feature subspace. Traditionally multiresolution analysis tools namely wavelets and curvelets have been used in the past for extracting and analyzing still images for recognition and classification tasks. Curvelet transform has gained significant popularity over wavelet based techniques...
In this work, we use the PCA based method to build a face recognition system with a recognition rate more than 97% for the ORL and 100% for the CMU databases. However, the main goal of this research is to identify the characteristics of face recognition rates while, i) the number of training and test data is varied; ii) the amount of noise in the training and test data is varied; iii) the level of...
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