Front-view Gait Recognition

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

doi:10.1109/btas.2008.4699356

Cited by 45 publications

(30 citation statements)

References 37 publications

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“…We can see that the topological signature provides better results for the lateral view. This agrees with [7].…”

Section: Methodssupporting

confidence: 81%

“…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: 97%

See 1 more Smart Citation

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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“…We can see that the topological signature provides better results for the lateral view. This agrees with [7].…”

Section: Methodssupporting

confidence: 81%

Section: Introductionmentioning

confidence: 97%

Gait-Based Gender Classification Using Persistent Homology

León

Cerri

García-Reyes

et al. 2013

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

View full text Add to dashboard Cite

show abstract

“…The use of frontal view video instead of the usual side view for gait recognition was also reported in some works. In [42], Goffredoat.el. used frontal view camera image sequence for gait recognition.…”

Section: Miscellaneous Methodsmentioning

confidence: 99%

Human Gait Recognition a Review

2018

IJAERD

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Abstract--The I. INTRODUCTIONHuman recognition by gait was motivated by the psychological studies of the motion perception by Johansson [1]. Johansson used Moving Light Display (MLD) to study the motion perception. Light bulbs were attached to the person"s joints in MLD experiments. Subjects were then filmed performing different types of motion activity such as dancing, walking and running in dark background. These films only show the collection of bright spots in a two dimensional plane and carry no structural information because the bright spots were not connected. The images of the bright spots were shown to different observers and it was noted that these scattered spots were meaningless to them. But when the films were played, the movement of the spots created impressions of a person walking, dancing and running etc. This shows that the relative movements of certain joints in the human body carry information about personal walking styles and dynamics. The position of light bulbs and corresponding point display is shown in Figure 1 for walking and running movement. It was also noted that the familiarity of an observer with a particular type of motion plays an important role in its recognition. The inverted MLDs were not recognized by observers as a walking or dancing pattern. Figure 1. Contours of (a) Walking and (b) Running subject with point configuration.

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“…Some recent researches have challenged gait recognition from the front view [16], [17], [18], [19], [20], [21], [22]. Soriano et al claims that front-view gait recognition has an advantage that it is compatible with the ability of humans to recognize their fellow beings from their front-view gaits [16].…”

Section: Introductionmentioning

confidence: 99%

Multi-angle Gait Recognition Based on Skeletal Tracking Data

Manabe

Matsumoto²,

Sugawara

2016

Journal of Information Processing

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Human gait recognition is one of the most important authentication technologies as it can often happen that people approach a computer system or a robot by walking. Therefore in this study, a multi-angle gait recognition method has been proposed by using skeletal tracking data, measured by an RGB-D camera. The proposed method includes a two stage process, which estimates an optimal gait angle view from the five discrete angles at the first stage and subsequently recognizes human gait based on the specific features for the respective gait angle views. In order to evaluate the proposed method, two types of experiments have been done: gait angle estimation and gait recognition. From the result of the first experiment, the best estimation of 97.4% accuracy has been achieved. In the second experiment, the best gait recognition accuracy was 96.4%. Finally the best gait recognition accuracy with the two stage process has been estimated as 93.9%.

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Front-view Gait Recognition

Cited by 45 publications

References 37 publications

Gait-Based Gender Classification Using Persistent Homology

Gait-Based Gender Classification Using Persistent Homology

Human Gait Recognition a Review

Multi-angle Gait Recognition Based on Skeletal Tracking Data

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