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Traditional face recognition methods such as Principal Components Analysis(PCA), Independent Component Analysis(ICA) and Linear Discriminant Analysis(LDA) are linear discriminant methods, but in the real situation, a lot of problems can't be linear discriminated; therefore, researchers proposed face recognition method based on kernel techniques which can transform the nonlinear problem of inputting...
The information fusion of face and palmprint biometrics using local features is investigated at feature level. The proposed method uses local information extracted from local region of biometric image which has rich statistical information. The texture of each region is processed using multiresolution analysis with different orientations and scales. The feature dimensionality of each region is reduced...
Kernel method is an effective technique in extracting nonlinear discriminative features. In this paper, we propose a new color face image recognition approach based on kernel holistic orthogonal analysis (KHOA) of discriminant transforms. Original color face images are mapped to high dimensional feature space by kernel function, then extract discriminant transforms of red, green, blue color image...
Face representation, including both feature extraction and feature selection, is the key issue for a successful face recognition system. In this paper, we propose a novel face representation scheme based on nonsubsampled contourlet transform (NSCT) and block-based kernel Fisher linear discriminant (BKFLD). NSCT is a newly developed multiresolution analysis tool and has the ability to extract both...
Localization of the eyes and mouth in face images is very important for accurate classification in automatic face recognition systems. The alignment of unknown face images with templates generally improves the performance of the face recognition system, and this process uses locations of the eyes and mouth. In this work, we compare different features (gray-level values, distance transform features,...
This paper investigates the possibility that uses Scale-Invariance Feature Transform (SIFT) feature for face identification. However, it is impossible to employ these SIFT keys,i.e. feature vectors, for identification directly, due to the space incompatible of such SIFT keys. To this end, the Bag-of-words (Bow) vector quantization introduced from scene or text classification is conducted for unifying...
This paper presents a novel face recognition method based on the contourlet for facial features representation and using an new kernel based algorithm, for discriminating purposes, namely kernel relevance weighted discriminant analysis (KRWDA). This nonlinear reduction dimension algorithm has several interesting characteristics. First, using kernel theory, it handles nonlinearity efficiently. Second,...
The proposed null Foley-Sammon transform (NFST) method based on the Gram-Schmidt orthogonalization successfully overcomes the so-called small sample size problem with high performance in terms of recognition accuracy and low computation cost, however, the NFST method is still a linear technique in nature, so a new nonlinear feature extraction method called kernel null Foley-Sammon transform (KNFST)...
A novel nonlinear feature extraction and recognition approach which is based on improved 2D Fisherface plus Kernel discriminant analysis is proposed. We provide an improved 2D Fisherface method that designs a new strategy to select appropriate 2D principal components and discriminant vectors, then we use 2D features to perform the Kernel discriminant analysis. The nearest neighbor classifier with...
Based on the attractive property such as shift invariance, good directional selectivity, limited redundancy and efficient computation of dual-tree complex wavelet transform, a novel face recognition method with combining of dual-tree complex wavelet transform and support vector machine is proposed in this paper. Firstly, it uses 2D dual-tree complex wavelet transform to decompose each face image into...
Based on Gabor wavelets, a novel multi-scale principal component analysis and support vector machine algorithm (MsPCA-SVM) for face recognition is proposed in this paper. Firstly, the Gabor wavelets transformation results including five scales and eight directions are calculated and 40 feature matrices which are reconstructed with the same scale and the same direction transform results of the different...
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