Fusion of Static and Dynamic Body Biometrics for Gait Recognition

Wang, Yunlong; Ning, Hui; Tan, Tieniu; Hu, Weiming

doi:10.1109/tcsvt.2003.821972

Cited by 332 publications

(136 citation statements)

References 27 publications

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“…The advantage of contour-based ellipse-fitting over region-based ellipse-fitting as in [10] is low computational complexity. Following the attempt in [5] which combines static and dy- namic gait signatures, STS-DM uses weight-based sum rule of score-level fusion to fuse the match scores obtained by different classifiers for subject identification. To demonstrate the efficacy of STS-DM in terms of robustness against most of the challenging factors that affect existing gait recognition systems, it is compared with several related stateof-the-art gait recognition methods which are referred to by their acronym for brevity.…”

Section: Related Workmentioning

confidence: 99%

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Gait recognition based on shape and motion analysis of silhouette contours

Das

Tjahjadi

2013

Computer Vision and Image Understanding

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This paper presents a three-phase gait recognition method that analyses the spatio-temporal shape and dynamic motion (STS-DM) characteristics of a human subject's silhouettes to identify the subject in the presence of most of the challenging factors that affect existing gait recognition systems. In phase 1, phase-weighted magnitude spectra of the Fourier descriptor of the silhouette contours at ten phases of a gait period are used to analyse the spatio-temporal changes of the subject's shape. A component-based Fourier descriptor based on anatomical studies of human body is used to achieve robustness against shape variations caused by all common types of small carrying conditions with folded hands, at the subject's back and in upright position. In phase 2, a full-body shape and motion analysis is performed by fitting ellipses to contour segments of ten phases of a gait period and using a histogram matching with Bhattacharyya distance of parameters of the ellipses as dissimilarity scores. In phase 3, dynamic time warping is used to analyse the angular rotation pattern of the subject's leading knee with a consideration of arm-swing over a gait period to achieve identification that is invariant to walking speed, limited clothing variations, hair style changes and shadows under feet. The match scores generated in the three phases are fused using weight-based score-level fusion for robust identification in the presence of missing and distorted frames, and occlusion in the scene. Experimental analyses on various publicly available data sets show that STS-DM outperforms several state-of-the-art gait recognition methods.

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Section: Related Workmentioning

confidence: 99%

“…Furthermore, gait characteristics change with ageing. Thus, a robust gait recognition method needs to analyse bio-mechanical gait characteristics via static and dynamic pose changes of gait as in [5,6,7].…”

Section: Introductionmentioning

confidence: 99%

Gait recognition based on shape and motion analysis of silhouette contours

Das

Tjahjadi

2013

Computer Vision and Image Understanding

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“…unwrapped silhouette [8]; silhouette similarity [9]; relational statistics [10]; self similarity [11]; key frame analysis [12]; frieze patterns [13]; area [14]; symmetry [15]; key poses [16] eigenspace sequences [19]; average silhouette [20]; moments [21]; ellipsoidal fits [22]; kinematic features [23]; gait style and content [24] stride parameters [25]; human parameters [26]; joint trajectories [27]; hidden Markov model [28][29]; articulated model [32]; dual oscillator [33]; linked feature trajectories [34] video oscillations [30] Accurate infrared target tracking is critical in many military weapons systems where common knowledge indicates that improving infrared target detection and tracking has the potential to simultaneously minimize unwanted collateral damage and maximize the probability of successful target elimination [35].…”

Section: Without Motion With Motionmentioning

confidence: 99%

Active Shape Model-Based Gait Recognition Using Infrared Images

Kim

Lee

Paik

2009

Communications in Computer and Information Science

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“…The above 2D models are similar to our 2D model mainly in that they both use human joints. Wang, Ning and Tan [7] propose a method to recognize and track a walker using 2D human model and both static and dynamic cues of body biometrics. Kakadiaris and Metaxas [4] present a 3D model-based method for motion estimation of human movement from multiple cameras.…”

Section: Introductionmentioning

confidence: 99%

Recognition and Tracking of the Members of a Moving Human Body

Panagiotakis

Tziritas

2004

Articulated Motion and Deformable Objects

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Abstract. We present a method to solve the human silhouette tracking problem using 18 major human points. We used: a simple 2D model for the human silhouette, a linear prediction technique for initializing major points search, geometry anthropometric constraints for determining the search area and color measures for matching human body parts. In addition, we propose a method to solve the problem of human members recognition and 18 major human points detection using silhouette. This result can be used to initialize a human tracking algorithm for real time applications. Our main purpose is to develop a low computation cost algorithm, which can be used independently of camera motion. The output of the tracking algorithm is the position of 18 major human points and a 2D human body extraction. In cases of low quality imaging conditions or low background contrast, the result may be worst. For these cases we defined an appropriate criterion concerning tracking ability.

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Fusion of Static and Dynamic Body Biometrics for Gait Recognition

Abstract: Abstract

Cited by 332 publications

References 27 publications

Gait recognition based on shape and motion analysis of silhouette contours

Gait recognition based on shape and motion analysis of silhouette contours

Active Shape Model-Based Gait Recognition Using Infrared Images

Recognition and Tracking of the Members of a Moving Human Body

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