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In order to further improve the recognition rate and computing efficiency of modular 2DPCA in face recognition, an improved modular 2DPCA method based on image segmentation is proposed. Firstly, segmentation of threshold value optimization is utilized to segment face image of training samples into several non-overlapping sub-image spaces so that the pixel number has uniform distribution in each sub-image...
To overcome the limitation of traditional face recognition methods for single sample face recognition, a single sample face recognition algorithm based on sample augments and MSD fusion is proposed in this paper. First, according to the facial symmetry theory, some relevant information of possible change could be extract to adapt to the future samples. A combination of the original training sample...
Based on the idea of collaborative representation, a novel approach CRC-GLasso is proposed for face recognition. Our main contributions lie in two aspects: 1) Instead of sparse representation, collaborative representation is employed to compute sparse representations of face images to solve the ‘lack of samples’ problem. The reason is that face images of different classes share similarities, and some...
A new multiple faces detection method based on facial features is proposed, which gets the face candidates with the help of skin color and makes use of wavelet express of images and the principal component analysis (PCA) to obtain the eigenvectors distinguishing faces and non-faces, and modifies Bayesian classifier to detect multiple faces of input images. ??, the parameter of the modified rules,...
Facial images with high dimension often belong to a manifold of intrinsically low dimension. Subspace methods utilize different algorithms to extract and analyze the underlying manifold for face recognition. Isomap is a recently proposed algorithm for manifold learning and nonlinear dimensionality reduction. However, since the Isomap is developed based on minimizing the reconstruction error with multi-dimensional...
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