Matrix computations (3rd ed.)
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Using the Discrete Cosine Transform
International Journal of Computer Vision - Special issue: Research at McGill University
Features for robust face-based identity verification
Signal Processing
A hidden markov model-based approach for face detection and recognition
A hidden markov model-based approach for face detection and recognition
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Block-level discrete cosine transform coefficients for autonomic face recognition
Block-level discrete cosine transform coefficients for autonomic face recognition
An assembled matrix distance metric for 2DPCA-based image recognition
Pattern Recognition Letters
Pattern Recognition Letters
Volume measure in 2DPCA-based face recognition
Pattern Recognition Letters
IEEE Transactions on Computers
Journal of Cognitive Neuroscience
Face recognition via two dimensional locality preserving projection in frequency domain
LSMS/ICSEE'10 Proceedings of the 2010 international conference on Life system modeling and simulation and intelligent computing, and 2010 international conference on Intelligent computing for sustainable energy and environment: Part III
On approaching 2D-FPCA technique to improve image representation in frequency domain
Proceedings of the Fourth Symposium on Information and Communication Technology
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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.