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Face recognition has achieved immense popularity in various fields because of its robustness and accuracy. But pose variation is still a major obstacle to overcome for effective face recognition in an uncontrolled environment. A wide variety of face recognition algorithms have been proposed in the past. In this paper we exhibit a review of some of the common algorithms that expect to conquer on the...
In this paper, a single hidden-layer feedforward fusion network is proposed for face identity verification. Essentially, the feature extraction, matching score calculation and fusion algorithm design steps are integrated and absorbed into a hidden layer of the model. Each hidden node works on the raw face image directly and produces an Euclidean distance based match score within the network. These...
A kind of algorithm called sparsity preserving-based local fisher discriminant analysis (SPLFDA) is proposed, which insulates sparsity preserving projections and local fisher discriminant analysis in the process of dimensionality reduction. It inherits the special character of geometrical structure preserving and neighborhood preserving. Experiments operated on UMIST, Yale and YaleB face dataset show...
Recently, semi-supervised learning, which uses unlabeled samples and supervised information together in learning process, has received much attention. Compared with class labels, pairwise constraints is a kind of supervised information which are more easily to obtain. In this paper, a new algorithm is proposed, called as SSGL, which preserves both the global (intrinsic) and local structure of the...
Discriminant Locality Preserving Projection (DLPP) has been successfully used as a dimensionality reduction technique to many classification problems, which incorporate discriminant information into Locality Preserving Projection (LPP) to improve recognition rate. However, in order to avoid small sample size problem, DLPP needs to reduce dimensions, which will lose some important discriminative information...
Learning from large, multi-class data sets poses great challenges to ensemble methods. The weak learner condition makes the conventional method inappropriate to handle multi-class classification, which leads to early termination of the training process. Also, elongated training time makes learning from large data set infeasible. To circumvent these issues, we present a novel method that integrates...
This system is needed to make the designing and research of algorithm more efficient. Two aspects are adopted in the designing of the system, the first is software designing and the second is the construction of image database with complex background. The software adopts several modules in the designing which is convenient for updating. Batch processing function is put into this software to simplify...
In this paper we present a novel approach of face identification by formulating the pattern recognition problem in terms of linear regression. Using a fundamental concept that patterns from a single object class lie on a linear subspace, we develop a linear model representing a probe image as a linear combination of class specific galleries. The inverse problem is solved using the least squares method...
Generalized singular value decomposition (GSVD) has been used in the literature for linear discriminant analysis (LDA) to solve the small sample size problem in pattern recognition. However, this algorithm suffers from excessive computational load when the sample dimension is high. In this paper, we present a modified version of the LDA/GSVD algorithm to enhance the computational efficiency, referred...
Generalized singular value decomposition (GSVD) has been used for linear discriminant analysis (LDA) to solve the small sample size problem in pattern recognition. However, this algorithm may suffer from the over-fitting problem. In this paper, we propose a novel orthogonalization technique for the LDA/GSVD algorithm to address the over-fitting problem. In this technique, an orthogonalization of the...
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