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A novel biometric feature extraction algorithm using two dimensional fisherface in 2DPCA subspace for face recognition

This paper describes a novel algorithm, 2D-FPCA, for face feature extraction and representation. The new algorithm fuses the two dimensional Fisherface method with the two dimensional principal component analysis (2DPCA). Our algorithm operates on the two dimensional image matrices. Therefore a total image covariance matrix can be constructed directly using the original image matrices and its eigenvectors are derived for feature extraction. Similarly, the between and the within image covariance matrices are constructed and transformed to a 2DPCA subspace. The result is that 2D-FPCA is faster and yields greater recognition accuracy. The ORL database is used as a benchmark. The new algorithm achieves a recognition rate of 95.50% compared to the recognition rate of 90.00% for the Fisherface method.