Feature Extraction using DCT fusion based on facial symmetry for enhanced face recognition
Authors:
- 1 October 2012
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 1-6
Abstract
Feature Extraction plays a very important role in Face Recognition technology. This paper proposes a novel Discrete Cosine Transform (DCT) fusion technique based on facial symmetry. Also proposed are DCT subset matrix selection based on aspect ratio of the image and pre-processing concepts, namely Local Histogram Equalization to remove illumination variation and Scale normalization using skin detection for colored images. The performance of proposed techniques is evaluated by computing the recognition rate and number of features selected for ORL, Extended Yale B and Color FERET databases.Keywords
This publication has 15 references indexed in Scilit:
- Face recognition using spectrum-based feature extractionApplied Soft Computing, 2012
- Contextual constraints based linear discriminant analysisPattern Recognition Letters, 2011
- Semi-random subspace method for face recognitionImage and Vision Computing, 2009
- Face recognition using FLDA with single training image per personApplied Mathematics and Computation, 2008
- Correlation Pattern Recognition for Face RecognitionProceedings of the IEEE, 2006
- Enhanced (PC)2A for face recognition with one training image per personPattern Recognition Letters, 2004
- Making FLDA applicable to face recognition with one sample per personPattern Recognition, 2004
- In search of illumination invariants2002
- Kernel Eigenfaces vs. Kernel Fisherfaces: Face recognition using kernel methods2002
- Discrete Cosine TransformIEEE Transactions on Computers, 1974