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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 propose a novel feature extraction scheme based on the multi-resolution curvelet transform for face recognition. The obtained curvelet coefficients act as the feature set for classification, and are used to train the ensemble-based discriminant learning approach, capable of taking advantage of both the boosting and LDA (BLDA) techniques. The proposed method CV-BLDA has been extensively...
In this paper we present a novel appearance based approach to the problem of face pose classification. This method suggests the subject-independent pose classification of face images using bilateral filtering and wavelet transform as preprocessing and isometric projection based subspace learning for the extracting of discriminant feature vectors. Our proposed method is evaluated on a large image set...
A new approach is proposed to improve face recognition in the paper. The accurate face is detected by the position relation of the face and the eyes. The face features are extracted using the Gabor wavelet and the Adaboost algorithm is used to detect the face and the eyes. In the actual detection of the face, the face is probably inclining, and then we correct the detected face according to the positions...
A improved face recognition method based on the large samples was proposed in the paper. The face and the eyes were detected using the Adaboost algorithm; and according to the distance and angle of the eyes, the accurate face could be gotten. For improving the robustness of face recognition against illumination, the face image was processed by the Retinex theory. The face feature was extracted using...
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