Automatic gait recognition

Nixon, Mark S.; Carter, John N.; Nash, Jason M.; Huang, Ping; Cunado, David; Stevenage, Sarah V.

doi:10.1049/ic:19990573

Cited by 97 publications

(79 citation statements)

References 40 publications

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“…The sequence of resulting silhouettes is analyzed to extract one subsequence of representation, which include at least a gait cycle [5].…”

Section: The Simplicial Complex ∂K(i)mentioning

confidence: 99%

“…1 http://www.cbsr.ia.ac.cn/english/GaitDatabases.asp1 persons and their gender at a distance, without any interaction from the subjects [5][6][7]. This fact can improve the performance of surveillance system intelligent, the analysis of customer information in trade centers, and it can reduce the false positive rate during reidentification of an individual on a wide network cameras.…”

Section: Introductionmentioning

confidence: 99%

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Gait-Based Gender Classification Using Persistent Homology

León

Cerri

García-Reyes

et al. 2013

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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Abstract. In this paper, a topological approach for gait-based gender recognition is presented. First, a stack of human silhouettes, extracted by background subtraction and thresholding, were glued through their gravity centers, forming a 3D digital image I. Second, different filters (i.e. particular orders of the simplices) are applied on ∂K(I) (a simplicial complex obtained from I) which capture relations among the parts of the human body when walking. Finally, a topological signature is extracted from the persistence diagram according to each filter. The measure cosine is used to give a similarity value between topological signatures. The novelty of the paper is a notion of robustness of the provided method (which is also valid for gait recognition). Three experiments are performed using all human-camera view angles provided in CASIA-B database 1 . The first one evaluates the named topological signature obtaining 98.3% (lateral view) of correct classification rates, for gender identification. The second one shows results for different human-camera distances according to training and test (i.e. training with a human-camera distance and test with a different one). The third one shows that upper body is more discriminative than lower body.

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“…The sequence of resulting silhouettes is analyzed to extract one subsequence of representation, which include at least a gait cycle [5].…”

Section: The Simplicial Complex ∂K(i)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Gait-Based Gender Classification Using Persistent Homology

León

Cerri

García-Reyes

et al. 2013

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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“…However, variations of the subject's clothes, footwear and hair style add complexity to gait recognition, and the subject's physical and mental conditions, e.g., leg injury, drunkenness and pregnancy, distort the walking pattern [2,4]. Gait recognition is also affected by occlusions in the scene, variations in viewpoint and walking speed, shape distortions due to carrying conditions, shadows under feet and change in ground surface.…”

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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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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“…Current state-of-the-art systems show that it is possible to recognise people using gait recognition by using silhouette or model based approaches [8].…”

Section: Introductionmentioning

confidence: 99%

Improving Gait Biometrics under Spoofing Attacks

Hadid

Ghahramani

Bustard

et al. 2013

Lecture Notes in Computer Science

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Abstract. Gait is a relatively new biometric modality which has a precious advantage over other modalities, such as iris and voice, in that it can be easily captured from a distance. While it has recently become a topic of great interest in biometric research, there has been little investigation into gait spoofing attacks where a person tries to imitate the clothing or walking style of someone else. We recently analysed for the first time the effects of spoofing attacks on silhouette based gait biometric systems and showed that it was indeed possible to spoof gait biometric systems by clothing impersonation and the deliberate selection of a target that has a similar build to the attacker. These findings are exploited in this current work for developing new solutions to cope with such possible spoofing attacks. We describe then in this paper an initial solution coping with gait spoofing attacks using part-based gait analysis. The proposed solution is thoroughly evaluated on the challenging USOU gait spoofing database collected within the EU Tabula Rasa project. The database consists of records of 22 subjects (14 male and 8 female), between 20-55 years old, walking through the Southampton tunnel in both their normal clothes and whilst wearing a common uniform. The obtained results are very promising and point out very interesting findings which can be used as a reference for developing more enhanced countermeasures by the research community.

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Automatic gait recognition

Cited by 97 publications

References 40 publications

Gait-Based Gender Classification Using Persistent Homology

Gait-Based Gender Classification Using Persistent Homology

Gait recognition based on shape and motion analysis of silhouette contours

Improving Gait Biometrics under Spoofing Attacks

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