Application of Bidirectional Two-dimensional Principal Component Analysis to Curvelet Feature Based Face Recognition

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 due to its improved directional and edge representation capability. In the past features extracted from curvelet subbands were dimensionally reduced using linear principal component analysis (PCA) for obtaining a representative feature set. The novelty of the proposed method lies in the application of 2DPCA to curvelet feature subspace by computing image covariance matrices of square training sample matrices in their original form and transposed form respectively to generate a more meaningful and enhanced feature vectors. Extensive experiments were performed using the proposed bidirectional 2DPCA based face recognition algorithm and superior performance is obtained in comparison with state of the art techniques.