Gait Recognition Using Hough Transform and Principal Component Analysis

2009 ◽  
pp. 363-370 ◽  
Author(s):  
Ling-Feng Liu ◽  
Wei Jia ◽  
Yi-Hai Zhu
Keyword(s):  
Principal Component Analysis ◽  
Hough Transform ◽  
Gait Recognition ◽  
Principal Component ◽  
Component Analysis

Comparing Ranking Methods for Tensor Components in Multilinear and Concurrent Subspace Analysis with Applications in Face Images

2015 ◽  
Vol 15 (01) ◽  
pp. 1550006 ◽  
Author(s):  
Tiene A. Filisbino ◽  
Gilson A. Giraldi ◽  
Carlos E. Thomaz
Keyword(s):  
Principal Component Analysis ◽  
Image Analysis ◽  
Gait Recognition ◽  
Principal Component ◽  
Component Analysis ◽  
Face Image ◽  
Image Databases ◽  
Subspace Analysis ◽  
Raw Data ◽  
Face Image Analysis

In the area of multi-dimensional image databases modeling, the multilinear principal component analysis (MPCA) and concurrent subspace analysis (CSA) approaches were independently proposed and applied for mining image databases. The former follows the classical principal component analysis (PCA) paradigm that centers the sample data before subspace learning. The CSA, on the other hand, performs the learning procedure using the raw data. Besides, the corresponding tensor components have been ranked in order to identify the principal tensor subspaces for separating sample groups for face image analysis and gait recognition. In this paper, we first demonstrate that if CSA receives centered input samples and we consider full projection matrices then the obtained solution is equal to the one generated by MPCA. Then, we consider the general problem of ranking tensor components. We examine the theoretical aspects of typical solutions in this field: (a) Estimating the covariance structure of the database; (b) Computing discriminant weights through separating hyperplanes; (c) Application of Fisher criterium. We discuss these solutions for tensor subspaces learned using centered data (MPCA) and raw data (CSA). In the experimental results we focus on tensor principal components selected by the mentioned techniques for face image analysis considering gender classification as well as reconstruction problems.

scholarly journals Principal component analysis for human gait recognition system

2019 ◽  
Vol 8 (2) ◽  
pp. 569-576
Author(s):  
Othman O. Khalifa ◽  
Bilal Jawed ◽  
Sharif Shah Newaj Bhuiyn
Keyword(s):  
Principal Component Analysis ◽  
Minimum Distance ◽  
Gait Recognition ◽  
Principal Component ◽  
Initial Step ◽  
Component Analysis ◽  
Recognition System ◽  
Human Gait ◽  
The One ◽  
Main Components

This paper represents a method for Human Recognition system using Principal Component Analysis. Human Gait recognition works on the gait of walking subjects to identify people without them knowing or without their permission. The initial step in this kind of system is to generate silhouette frames of walking human. A number of features couldb be exytacted from these frames such as centriod ratio, heifht, width and orientation. The Principal Component Analysis (PCA) is used for the extracted features to condense the information and produces the main components that can represent the gait sequences for each waiking human. In the testing phase, the generated gait sequences are recognized by using a minimum distance classifier based on eluclidean distance matched with the one that already exist in the database used to identify walking subject.

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.

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