In this paper, a new technique called Two Dimensional Gabor Principle Component Analysis (2DGPCA) is derived and implemented for image representation and recognition. The 2DGPCA method addresses the problems of feature extraction, feature selection and classification. In this approach, the Gabor wavelets are used to extract facial features. The Principle Component Analysis (PCA) is then applied directly on the Gabor transformed image matrices in order to remove redundant information and form an efficient representation more suitable for face recognition. During classification, the Euclidean classifier is explored for simplicity and robustness in the presence of facial variations. The justification behind combining the 2D Gabor wavelets and the PCA is that Gabor transformed face images contain spatial locality, scale and orientation. These images are robust to variations due to pose, expression and scale, thus, making them most suitable for face representation and recognition. The proposed 2DGPCA method was tested on face recognition using the ORL database, where the images vary in expression, pose, and scale. In particular, the 2DGPCA method achieves 97.5% face recognition accuracy when using feature matrices of size 40×5×1 incorporating 5 different scales and 8 orientations compare to 93.5% and 94.5% with feature matrices of size 56×8 and 56×6 for the 2DPCA and 2DFLD method.
|Title of host publication||Proceedings of the 6th International Symposium Communication Systems, Networks and Digital Signal Processing, CSNDSP 08|
|Number of pages||5|
|Publication status||Published - 29 Aug 2008|
|Event||6th International Symposium Communication Systems, Networks and Digital Signal Processing, CSNDSP 08 - Graz, Austria|
Duration: 23 Jul 2008 → 25 Jul 2008
|Conference||6th International Symposium Communication Systems, Networks and Digital Signal Processing, CSNDSP 08|
|Period||23/07/08 → 25/07/08|