An improved biometrics technique based on metric learning approach

Kernel coupled distance metric learning for gait recognition and face recognition

This study, for the first time, developed an adaptive neural networks (NNs) formulation for the two-dimensional principal component analysis (2DPCA), whose space complexity is far lower than that of its statistical version. Unlike the NNs formulation of principal component analysis (PCA, i.e., 1DPCA), the solution with lower iteration in nature aims to directly deal with original image matrices. We also put forward the consistence in the conceptions of `eigenfaces' or `eigengaits' in both 1DPCA and 2DPCA neural networks. To evaluate the performance of the proposed NN, the experiments were carried out on AR face database and on 64 × 64 pixels gait energy images on CASIA(B) gait database. The less reconstruction error was exploited using the proposed NN in the condition of a large sample set compared to adaptive estimation of learning algorithms for NNs of PCA. On the contrary, if the sample set was small, the proposed NN could achieve a higher residue error than PCA NNs. The amount of calculation for the proposed NN here could be smaller than that for the PCA NNs on the feature extraction of the same image matrix, which represented an efficient solution to the problem of training images directly. On face and gait recognition tasks, a simple nearest neighbor classifier test indicated a particular benefit of the neural network developed here which serves as an efficient alternative to conventional PCA NNs.

An adaptive neural networks formulation for the two-dimensional principal component analysis

Due to the restriction of acquisition equipment and other reasons, the finger vein line appeared broken, torsion, translation and other deformation in local area. Therefore, the traditional methods got low recognition accuracy. In this paper, combining the advantages of modular method and bi-directional weighted B2DPCA with eigenvalue normalization; the bidirectional two-dimensional principal component analysis (B2DPCA) algorithm is improved. The bi-directional weighted modular B2DPCA (BWMB2DPCA) algorithm is proposed in order to extract the local feature of finger vein effective. The nearest neighbor classifier was used to distinguish different fingers. Experimental results show that whether in the little training samples, or in the multiple training samples, this method can obtain a better recognition effect than 2DPCA, B2DPCA, WB2DPCA and MB2DPCA.

Bi-directional weighted modular B2DPCA for finger vein recognition

The support vector machine (SVM) algorithm is applied to segment caudatum, putamen and pallidum region in brain magnetic resonance imaging (MRI) in this paper. A multi-classification SVM based on two-classification SVM is proposed in the segmentation processing. Firstly, the rough sets (RS) and principal component analysis (PCA) are separately used for reducing the dimension number of the high dimensional feature vectors extracted from Brain MRI. Secondly, the multi-classification SVM are adopted to classify for the non-reduction high dimensional feature vectors and the reduced feature vectors respectively. Finally, the classification performance of the multi-classification SVM is analyzed according to the false alarm probability, the false dismissal probability and the segmentation accuracy. A great deal of experimental results shows that the segmentation accuracy of the proposed multi-classification SVM segmentation is the highest compared with the k-means clustering (KMC), the fuzzy c-mean clustering segmentation (FCMS), k-nearest neighbor method (KNN), the Bayes classifier and the radial basis function neural network (RBFNN) segmentation for any feature vectors. However, the high segmentation accuracy is gotten at the cost of high computational complexity.

Research on the segmentation of tiny multi-target in brain tissues based on support vector machines

A gait recognition method based on subpattern complete two dimensional principal component analysis (SpC2DPCA) is proposed.Firstly,gait energy images are divided into small sub-images and any ineffectual subblock is removed adaptively.Then,C2DPCA approach is applied to every sub-image directly to obtain sub-feature.Finally,those sub-features are synthesized into the whole for subsequent classification using the nearest neighbor classifier.The proposed gait recognition method is evaluated on the CASIA gait database,and the number of sub-pattern division is determined through experiments.The experimental results demonstrate that the performance of SpC2DPCA is obviously superior to that of C2DPCA.The proposed method is effective in local feature extraction and person identification with clothes changing,backpacking and direction of gait changing.

Subpattern-Based Complete Two Dimensional Principal Component Analysis for Gait Recognition

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Subpattern-Based Complete Two Dimensional Principal Component Analysis for Gait Recognition