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Face recognition provides a challenging issue in the domain of analyzing images. In this paper a novel approach for face recognition using hybrid SIFT-SVM is proposed. The current database is divided into two parts; training and testing database. The SIFT feature will be created for each training images and the keypoints are computed; then the SVM is applied for the matching process for test images...
Facial Expression Recognition has mostly been done on frontal or near frontal faces. However, most of the faces in real life are non-frontal. This paper deals with in-plane rotation of faces in image sequences and considers the six universal facial expressions. The proposed approach does not need to rotate the image to frontal position. FER by rotating images to frontal is sensitive to determination...
This paper proposes a robust faces recognition method based on the phase spectrum features of the local normalized image. The principal components analysis (PCA) and the support vector machine (SVM) are used in the classification stage. We evaluate how the proposed method is robust to illumination, occlusion and expressions using "AR face database", which includes the face images of 109...
Noise-resistance capability is a very important issue to signal processing systems as well as machine learning applications. In this work, we present a new kernel that is highly robust against outliers and random noises. By incorporating a robust ρ-function into the distance metric, the derived robust kernel was shown to be very insensitive to the influence of outlier elements. In the experiments,...
In this paper, support vector machines (SVMs) are investigated for visual gender classification with low-resolution "thumbnail" faces (21-by-12 pixels) processed from 1,755 images from the FERET face database. The performance of SVMs (3.4% error) is shown to be superior to traditional pattern classifiers (linear, quadratic, Fisher linear discriminant, nearest neighbor) as well as more modern...
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