Exploratory factor analysis of gait recognition

Bouchrika, Imed; Nixon, Mark S.

doi:10.1109/afgr.2008.4813395

Cited by 68 publications

(36 citation statements)

References 21 publications

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“…A study [9] has reported a recognition rate of just 3% for the combination of the following covariate factors: clothes, shoes and time. Another study [7] reported a significant drop in performance (87% to 60%) when subjects wore trench coat on top of their normal clothes.…”

Section: B Effect Of Clothesmentioning

confidence: 99%

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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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Section: B Effect Of Clothesmentioning

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.…”

mentioning

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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“…This extends recent research studies by on covariate analysis [4] and view-point invariant gait recognition [5]. A novel reconstruction method is being employed to rectify and normalize gait features recorded from different view-point into the side-view plane and therefore exploit such data for recognition.…”

Section: Introductionmentioning

confidence: 92%

Covariate Analysis for View-Point Independent Gait Recognition

Bouchrika

Goffredo

Carter

et al. 2009

Advances in Biometrics

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Abstract. Many studies have shown that gait can be deployed as a biometric. Few of these have addressed the effects of view-point and covariate factors on the recognition process. We describe the first analysis which combines view-point invariance for gait recognition which is based on a model-based pose estimation approach from a single un-calibrated camera. A set of experiments are carried out to explore how such factors including clothing, carrying conditions and view-point can affect the identification process using gait. Based on a covariate-based probe dataset of over 270 samples, a recognition rate of 73.4% is achieved using the KN N classifier. This confirms that people identification using dynamic gait features is still perceivable with better recognition rate even under the different covariate factors. As such, this is an important step in translating research from the laboratory to a surveillance environment.

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“…Hence, it is also used for exploratory factor analysis of gait recognition [5]. The recently published Soton Temporal database [23] contains the largest variations, up to 9 months, in elapsed time.…”

Section: Related Workmentioning

confidence: 99%

The OU-ISIR Gait Database Comprising the Treadmill Dataset

Makihara

Mannami

Tsuji

et al. 2012

IPSJ Transactions on Computer Vision and Applications

197

120

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This paper describes a large-scale gait database comprising the Treadmill Dataset. The dataset focuses on variations in walking conditions and includes 200 subjects with 25 views, 34 subjects with 9 speed variations from 2 km/h to 10 km/h with a 1 km/h interval, and 68 subjects with at most 32 clothes variations. The range of variations in these three factors is significantly larger than that of previous gait databases, and therefore, the Treadmill Dataset can be used in research on invariant gait recognition. Moreover, the dataset contains more diverse gender and ages than the existing databases and hence it enables us to evaluate gait-based gender and age group classification in more statistically reliable way.

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Exploratory factor analysis of gait recognition

Cited by 68 publications

References 21 publications

The Effect of Time on Gait Recognition Performance

The Effect of Time on Gait Recognition Performance

Covariate Analysis for View-Point Independent Gait Recognition

The OU-ISIR Gait Database Comprising the Treadmill Dataset

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