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An efficient face recognition system should recognize faces in different views and poses. The efficiency of a human face recognition system depends on the capability of face recognition in presence of changes in the appearance of face due to expression, pose and illumination. A novel algorithm which utilizes the combination of texture and depth information based on Modular PCA to overcome the problem...
To solve the challenging problem of face recognition under varying illumination conditions, we propose in this paper a novel LBP operator which we refer to as Local Binary Patterns with Circle Threshold (CT-LBP) operator. The CT-LBP operator can keep more discriminating information than the original LBP operator without losing the simplicity and effectivity of the original LBP operator. Extensive...
An efficient illumination invariant face recognition method based on two-stage two dimensional linear discriminant analysis (2S2DLDA) is presented in this paper. The proposed method uses a reflectance-illumination model (RI-Model) based on maximum filter to obtain illumination invariants of an image. Various combinations of two dimensional feature extraction techniques (PCA, 2DPCA family and 2DLDA...
This paper presents a novel scheme for feature extraction for face recognition by fusing local and global discriminant features. The facial changes due to variations of pose, illumination, expression, etc. are often appeared only some regions of the whole face image. Therefore, global features extracted from the whole image fail to cope with these variations. To address these problems, face images...
With illumination varying condition, face features gotten from image is distorted nonlinearly by variant lighting intensity and direction, so face recognition becomes very difficult. According the "common assumption" that illumination vary slowly and the face intrinsic feature (including 3D surface and reflectance) vary rapidly in local area, we can consider that high frequency features...
In this paper, we study face recognition using principal component analysis (PCA) and linear discriminant analysis (LDA) under illumination variations. A modified census transform (MCT) is applied as preprocessing step to compensate illumination variations, and then PCA and LDA are employed to find lower-dimensional subspaces for face recognition. Distances between training and testing images are...
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