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In face recognition, a way to enhance the discriminability is to provide effective feature representation. Dual-Tree Complex Wavelet transform (DT-CWT) provides a local multiscale description of images with good directional selectivity and shift invariance, and is robust to illumination variations and facial expression changes. In this paper, we propose a novel approach to face feature extraction...
A face recognition method that based on Gabor wavelet transform and fractal is proposed, Since Gabor feature is robust to illumination and expression variations and has been successfully used in face recognition area. First, the proposed method decomposes the normalized face image by convolving the face image with multi-scale and multi-orientation Gabor filters to extract their corresponding Gabor...
The face recognition task involves extraction of unique features from the human face. Manifold learning methods are proposed to project the original data into a lower dimensional feature space by preserving the local neighborhood structure. LPP should be seen as an alternative to Principal Component Analysis (PCA). When the high dimensional data lies on a low dimensional manifold embedded in the ambient...
In this paper we present a novel approach to recognition of faces in frontal color images. It involves face extraction, creation of face models with wavelet packet decomposition for dimensionality reduction, Principal Component Analysis (PCA) of the decomposed faces, Linear Discriminant Analysis (LDA) over the PCA subspace, neural classifiers with radial basis functions for each modality and combination...
A Gabor wavelet based modular PCA approach for face recognition is proposed in this paper. The proposed technique improves the efficiency of face recognition, under varying illumination and expression conditions for face images when compared to traditional PCA techniques. In this algorithm the face images are divided into smaller sub-images called modules and a series of Gabor wavelets at different...
We propose a face recognition model consisting of the following stages: facial feature localization (23 essential points, corresponding to eyes, mouth, nose, and face boundary); feature representation by Gabor wavelet based filtering (GWF); dimensionality reduction using principal component analysis (PCA); neural classification using concurrent self-organizing maps (CSOM). For the ORL face database,...
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