Abstract
In this contribution, a Transform Domain Two-Dimensional Principal Component Analysis algorithm employing Vector Quantization (TD2DPCA/VQ) is presented for facial recognition, particularly for large databases. The algorithm has attractive properties with respect to storage requirements in the training mode and the computational complexity in the testing mode. The experimental results obtained by applying the new algorithm to the ORL database confirmed the significant reduction in the storage and computational requirements while improving the excellent recognition accuracy of the spatial 2DPCA method.
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