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This paper proposes a design to implement Intelligent Examination subsystem in the Internet Plus environment, which uses structure model combining B/S and C/S, JSP technology and SSH frames. Student examination terminal facilitates automatic acquisition and maintenance of exam candidate's face image for automatic authentication of its identity, automatically obtaining examination papers' information,...
Marginal fisher analysis (MFA) is an effective approach for feature extraction and recognition. However, an intrinsic limitation existed in MFA is that it deemphasizes the importance of the distant points, which may degrade the recognition performance. In this paper, a novel algorithm called graph discriminant embedding (GDE) is proposed to overcome the limitation. GDE maintains the good property...
The recent emerging sparse coding (SC) algorithms do not take local manifold structure of samples into consideration, while graph regularized sparse coding (GraphSC) algorithm only constrains the locality consistency of samples. Furthermore, the graph construction approach based on k-nearest-neighbor usually pre-defines the number of neighbors for all the samples, which may fails to fit the intrinsic...
Considering inherent limitations of such locality-based dimensionality reduction methods as unsupervised discriminant projection (UDP), a novel manifold-base feature extraction method, called locally supervised discriminant analysis in kernel space, is proposed in the paper. It is a locally nonlinear and supervised dimensionality reduction method, which takes into account the locality, kernel mapping...
A modified independent component analysis method, termed the matrix-based modular independent component analysis (MMICA), is developed in this paper. The main idea of the proposed method is that each of all facial images is first partitioned into many subimages. Every subimage is regarded as a new training sample, by which a new set of training samples is formed. Since the dimensionality of each of...
The paper first gives a method for removing the thousands of noisy points by using neighborhood centroid constrained fairing algorithm. And then, uses the 3D point clouds to reconstruct the face surface. At last, we could extract the facial feature.
This article presents a new SVM (support vector machine) fast learning algorithm which is based on the boundary vector. The speed of this algorithm has been improved considerably than traditional support vector machine,the requirement of memory space has also been obviously reduced. At the same time,because the support vector won't be lost in the process of selecting the boundary vector, So the performance...
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