Advances in automatic gait recognition

Nixon, Mark S.; Carter, J.N.

doi:10.1109/afgr.2004.1301521

Cited by 69 publications

(21 citation statements)

References 52 publications

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“…Gait is a new biometric with a number of benefits arising from its non-intrusive nature and the possibility of use at a distance [21,3,7,9,10] . Hence, the analysis of the different covariate factors becomes essential to quantify the intrusiveness of gait recognition which will be the focus of this paper.…”

Section: Introductionmentioning

confidence: 99%

Exploratory factor analysis of gait recognition

Bouchrika

Nixon

2008

2008 8th IEEE International Conference on Automatic Face &Amp; Gesture Recognition

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Many studies have now shown that it is possible to recognize people by the way they walk. As yet there has been little formal study of the effects of covariates on the recognition process. We show how these factors can separately affect the walking pattern. Further we assess the contribution and discriminatory significance of the gait dynamics used for recognition. Based on a covariate-based probe dataset of 440 samples, a high recognition rate of 73.4% is achieved using the KN N classifier. This is to confirm that people identification using dynamic gait features is still perceivable with better recognition rate even under the different covariate factors.

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Section: Introductionmentioning

confidence: 99%

Exploratory factor analysis of gait recognition

Bouchrika

Nixon

2008

2008 8th IEEE International Conference on Automatic Face &Amp; Gesture Recognition

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“…Much of the work on gait analysis has focused on 2D image representations such as silhouettes, raw images, or features derived directly from image [6,7,16,18,27,31,37,39]. Here we focus on 3D body joint angles which have also received a great deal of attention for biometric person identification [8,17,27,55] emotion modeling [2,56] and tracking [30,53,60,59,65].…”

Section: Previous Workmentioning

confidence: 99%

Representing cyclic human motion using functional analysis

Ormoneit¹,

Black

Hastie

et al. 2005

Image and Vision Computing

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We present a robust automatic method for modeling cyclic 3D human motion such as walking using motion-capture data. The pose of the body is represented by a time series of joint angles which are automatically segmented into a sequence of motion cycles. The mean and the principal components of these cycles are computed using a new algorithm that enforces smooth transitions between the cycles by operating in the Fourier domain. Key to this method is its ability to automatically deal with noise and missing data. A learned walking model is then exploited for Bayesian tracking of 3D human motion.

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“…Extracting gait features is a common method in gait analysis [3][4][5]. There are different ways to extract features for different purposes, for example, gender recognition, age effect, individual identification, and medical condition analysis.…”

Section: Introductionmentioning

confidence: 99%

Extraction of bodily features for gait recognition and gait attractiveness evaluation

Hong

Kang

Price

2012

Multimed Tools Appl

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Although there has been much previous research on which bodily features are most important in gait analysis, the questions of which features should be extracted from gait, and why these features in particular should be extracted, have not been convincingly answered. The primary goal of the study reported here was to take an analytical approach to answering these questions, in the context of identifying the features that are most important for gait recognition and gait attractiveness evaluation. Using precise 3D gait motion data obtained from motion capture, we analyzed the relative motions from different body segments to a root marker (located on the lower back) of 30 males by the fixed root method, and compared them with the original motions without fixing root. Some particular features were obtained by principal component analysis (PCA). The left lower arm, lower legs and hips were identified as important features for gait recognition. For gait attractiveness evaluation, the lower legs were recognized as important features.

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Advances in automatic gait recognition

Abstract: Abstract

Cited by 69 publications

References 52 publications

Exploratory factor analysis of gait recognition

Exploratory factor analysis of gait recognition

Representing cyclic human motion using functional analysis

Extraction of bodily features for gait recognition and gait attractiveness evaluation

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