2DPCA-based techniques in DCT domain for face recognition. (16th July 2009) - Bengherabi, Messaoud; Mezai, Lamia; Harizi, Farid; Guessoum, Abderrazak; Cheriet, Mohamed
In this paper, we introduce two-dimensional PCA (2DPCA), diagonal principal component analysis (DiaPCA) and DiaPCA+2DPCA in DCT domain for the aim of face recognition. The 2D discrete cosine transform (2D DCT) transform has been used as a pre-processing step, then 2DPCA, DiaPCA and DiaPCA+2DPCA are applied on the upper left corner block of the global 2D DCT transform matrix of the original images. The Olivetti Research Laboratory (ORL) and YALE face databases are used to compare the proposed approach with the conventional one without DCT under four matrix similarity measures: Frobenuis, Yang, assembled matrix distance (AMD) and volume measure (VM). The experiments show that in addition to the significant gain in both the training and testing times, the recognition rate using 2DPCA, DiaPCA and DiaPCA+2DPCA in DCT domain is generally better or at least competitive with the recognition rates obtained by applying these three 2D appearance-based statistical techniques directly on the raw pixel images; especially, under the VM similarity measure.