Covariate Analysis for View-Point Independent Gait Recognition

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

doi:10.1007/978-3-642-01793-3_100

Cited by 22 publications

(9 citation statements)

References 11 publications

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“…Methods based on feature extraction using silhouettes or contour of silhouettes have been frequently used [3,10,6,1]. However, many silhouettes obtained during the process are incomplete due to illumination changes, occlusions, and others.…”

Section: Introductionmentioning

confidence: 99%

Human Gait Identification Using Persistent Homology

Lamar

García-Reyes

González-Dı́az

2012

Lecture Notes in Computer Science

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Abstract. This paper shows an image/video application using topological invariants for human gait recognition. Using a background subtraction approach, a stack of silhouettes is extracted from a subsequence and glued through their gravity centers, forming a 3D digital image I. From this 3D representation, the border simplicial complex ∂K(I) is obtained. We order the triangles of ∂K(I) obtaining a sequence of subcomplexes of ∂K(I). The corresponding filtration F captures relations among the parts of the human body when walking. Finally, a topological gait signature is extracted from the persistence barcode according to F . In this work we obtain 98.5% correct classification rates on CASIA-B database 1 .

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Section: Introductionmentioning

confidence: 99%

Human Gait Identification Using Persistent Homology

Lamar

García-Reyes

González-Dı́az

2012

Lecture Notes in Computer Science

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“…Studies quantifying the effect of covariates on the performance of gait recognition algorithms include [6]- [8]. Covariate factors can be related either to the subject itself (e.g., different clothing) or to the environment (e.g., different walking surface).…”

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confidence: 99%

“…Many studies, such as [6], [7] and [21], do not consider temporal data and focus solely on data acquired on the same day.…”

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confidence: 99%

The Effect of Time on Gait Recognition Performance

Matovski

Nixon

Mahmoodi

et al. 2012

IEEE Trans.Inform.Forensic Secur.

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Abstract-Many studies have shown that it is possible to recognize people by the way they walk. However, there are a number of covariate factors that affect recognition performance. The time between capturing the gallery and the probe has been reported to affect recognition the most. To date, no study has isolated the effect of time, irrespective of other covariates. Here, we present the first principled study that examines the effect of elapsed time on gait recognition. Using empirical evidence we show for the first time that elapsed time does not affect recognition significantly in the short-medium term. This finding challenges the existing view in the literature that time significantly affects gait recognition. We employ existing gait representations on a novel dataset captured specifically for this study. By controlling the clothing worn by the subjects and the environment, a Correct Classification Rate (CCR) of 95% has been achieved over the longest time period yet considered for gait on the largest ever temporal dataset. Our results show that gait can be used as a reliable biometric over time and at a distance if we were able to control all other factors such as clothing, footwear etc. We have also investigated the effect of different type of clothes, variations in speed and footwear on the recognition performance. The purpose of these experiments is to provide an indication of why previous studies (employing the same techniques as this study) have achieved significantly lower recognition performance over time. Our experimental results show that clothing and other covariates have been confused with elapsed time previously in the literature. We have demonstrated that clothing drastically affects the recognition performance regardless of elapsed time and significantly more than any of the other covariates that we have considered here.

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“…The covariate factors are of equal concern and there is interest in the degree that external factors impede recognition by gait. Analysis of covariate factors including clothing and footwear has shown that wearing a trench coat or the wearing of flip flops can significantly affect recognition performance [1]. This is not surprising in that the wearing of clothes that obscure the whole body will naturally obviate any gait biometric.…”

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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Covariate Analysis for View-Point Independent Gait Recognition

Cited by 22 publications

References 11 publications

Human Gait Identification Using Persistent Homology

Human Gait Identification Using Persistent Homology

The Effect of Time on Gait Recognition Performance

Improving Gait Biometrics under Spoofing Attacks

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