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Y.-C. Lee and C.-H. Chen, “Feature Extraction for Face Recognition Based on Gabor Filters and Two-Dimensional Locality Preserving Projections,” Proceedings of the 5th IEEE Conference on Intelligent Information Hiding and Multimedia Signal Processing, Kyoto, 12-14 September 2009, pp. 106-109.

has been cited by the following article:

• TITLE: Face Representation Using Combined Method of Gabor Filters, Wavelet Transformation and DCV and Recognition Using RBF

  AUTHORS: Kathirvalavakumar Thangairulappan, Jebakumari Beulah Vasanthi Jeyasingh

  KEYWORDS: Feature Extraction; Gabor Wavelet; Wavelet Transformation; Discriminative Common Vector; Radial Basis Function Neural Network

  JOURNAL NAME: Journal of Intelligent Learning Systems and Applications, Vol.4 No.4, November 28, 2012

  ABSTRACT: An efficient face representation is a vital step for a successful face recognition system. Gabor features are known to be effective for face recognition. The Gabor features extracted by Gabor filters have large dimensionality. The feature of wavelet transformation is feature reduction. Hence, the large dimensional Gabor features are reduced by wavelet transformation. The discriminative common vectors are obtained using the within-class scatter matrix method to get a feature representation of face images with enhanced discrimination and are classified using radial basis function network. The proposed system is validated using three face databases such as ORL, The Japanese Female Facial Expression (JAFFE) and Essex Face database. Experimental results show that the proposed method reduces the number of features, minimizes the computational complexity and yielded the better recognition rates.