A Subblock Matrix Approach to Data-Reduction in Gait Recognition

2011 ◽  
Vol 181-182 ◽  
pp. 902-907
Author(s):  
Xian Ye Ben ◽  
Shi An ◽  
Jian Wang ◽  
Hai Yang Liu
Keyword(s):  
Principal Component Analysis ◽  
Data Reduction ◽  
Nearest Neighbor ◽  
Recognition Accuracy ◽  
Gait Recognition ◽  
Principal Component ◽  
Component Analysis ◽  
Two Dimensional ◽  
Redundant Data ◽  
Novel Method

We propose a novel method for data reduction in gait recognition, called Subblock Complete Two Dimensional Principal Component Analysis (SbC2DPCA). GEIs were divided into smaller sub-images and redundant subblocks were adaptively removed. Complete Two Dimensional Principal Component Analysis (C2DPCA) was then applied to every sub-image directly, to acquire a set of projection sub-vectors for both row and column directions and these were synthesized into whole features for subsequent classification using nearest neighbor classifier. We evaluate the proposed gait recognition method on the CASIA gait database. The experimental results and analysis show the recognition accuracy of SbC2DPCA to be superior to C2DPCA, with C2DPCA being a special case of SbC2DPCA. The novelty of the proposed method lies in the adaptive removal of redundant data while extracting local features. This translates to data reduction with very minimal loss of information, as demonstrated by the remarkable recognition accuracy when subjects change clothing or have a backpack.

scholarly journals Patch-Based Principal Component Analysis for Face Recognition

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Tai-Xiang Jiang ◽  
Ting-Zhu Huang ◽  
Xi-Le Zhao ◽  
Tian-Hui Ma
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Nearest Neighbor ◽  
Spatial Information ◽  
Principal Component ◽  
Component Analysis ◽  
Two Dimensional ◽  
Face Images ◽  
Pca Method ◽  
Local Spatial Information

We have proposed a patch-based principal component analysis (PCA) method to deal with face recognition. Many PCA-based methods for face recognition utilize the correlation between pixels, columns, or rows. But the local spatial information is not utilized or not fully utilized in these methods. We believe that patches are more meaningful basic units for face recognition than pixels, columns, or rows, since faces are discerned by patches containing eyes and noses. To calculate the correlation between patches, face images are divided into patches and then these patches are converted to column vectors which would be combined into a new “image matrix.” By replacing the images with the new “image matrix” in the two-dimensional PCA framework, we directly calculate the correlation of the divided patches by computing the total scatter. By optimizing the total scatter of the projected samples, we obtain the projection matrix for feature extraction. Finally, we use the nearest neighbor classifier. Extensive experiments on the ORL and FERET face database are reported to illustrate the performance of the patch-based PCA. Our method promotes the accuracy compared to one-dimensional PCA, two-dimensional PCA, and two-directional two-dimensional PCA.

The Symmetrical Variation of 2DPCA for Face Recognition

2011 ◽  
Vol 255-260 ◽  
pp. 2004-2008
Author(s):  
Zeng Yue ◽  
Da Zheng Feng ◽  
Xiong Li
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Recognition Accuracy ◽  
Principal Component ◽  
Component Analysis ◽  
Two Dimensional ◽  
Recognition Time ◽  
Relationship Of ◽  
The Relationship

This paper first discusses the relationship of Principal Component Analysis (PCA) and two-dimensional PCA (2DPCA). For 2DPCA eliminating the some covariance information which can be useful for recognition, The symmetrical Variation of 2DPCA for Face recognition (V2DPCA) is proposed. These experiments on both of ORL face bases shows improvement in recognition accuracy, fewer coefficients and recognition time over 2DPCA, and this algorithm is also superior to the traditional eigenfaces, ICA and Kernel eigenfaces in terms of the recognition accuracy.

scholarly journals Moving objects classification via category-wise two-dimensional principal component analysis

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Falah Alsaqre ◽  
Osama Almathkour
Keyword(s):  
Principal Component Analysis ◽  
Moving Objects ◽  
Nearest Neighbor ◽  
Principal Component ◽  
Feature Space ◽  
Classification Problem ◽  
Component Analysis ◽  
Classification Model ◽  
Vehicle Classification ◽  
Two Dimensional

Classifying moving objects in video sequences has been extensively studied, yet it is still an ongoing problem. In this paper, we propose to solve moving objects classification problem via an extended version of two-dimensional principal component analysis (2DPCA), named as category-wise 2DPCA (CW2DPCA). A key component of the CW2DPCA is to independently construct optimal projection matrices from object-specific training datasets and produce category-wise feature spaces, wherein each feature space uniquely captures the invariant characteristics of the underlying intra-category samples. Consequently, on one hand, CW2DPCA enables early separation among the different object categories and, on the other hand, extracts effective discriminative features for representing both training datasets and test objects samples in the classification model, which is a nearest neighbor classifier. For ease of exposition, we consider human/vehicle classification, although the proposed CW2DPCA-based classification framework can be easily generalized to handle multiple objects classification. The experimental results prove the effectiveness of CW2DPCA features in discriminating between humans and vehicles in two publicly available video datasets.

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