Clothing-invariant gait identification using part-based clothing categorization and adaptive weight control

Hossain, Md. Altab; Makihara, Yasushi; Wang, Junqiu; Yagi, Yasushi

doi:10.1016/j.patcog.2009.12.020

Cited by 149 publications

(108 citation statements)

References 19 publications

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“…Therefore, the method in [13] applies part-based strategy to adaptively assign more weight to body parts that remain unaffected due to clothing variation and less weight to affected body parts based on a probabilistic framework. However, it is unrealistic to train the model with all known clothing types in realistic scenario.…”

Section: Related Workmentioning

confidence: 99%

“…Our method is evaluated on the dataset B which comprises 68 subjects with up to 32 combinations of different types of clothing. Table 2 shows these clothing combinations based on the 15 different types of clothes used in constructing the dataset [13]. The dataset defines the combination of regular pant and full shirt as the standard clothing type (type 9).…”

Section: Ou-isir Treadmill Gait Datasetmentioning

confidence: 99%

“…Unlike the method in [13], the goal of our method is to demonstrate robustness against unpredictable variation in clothing.…”

Section: Ou-isir Treadmill Gait Datasetmentioning

confidence: 99%

“…Hence, unlike [13], we do not use the training dataset to train our system with all possible types of clothing combinations. We evaluated GEI on OU-ISIR dataset B using gallery and probe sets each comprising 48 subjects to compare with the proposed method.…”

Section: Ou-isir Treadmill Gait Datasetmentioning

confidence: 99%

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Clothing and carrying condition invariant gait recognition based on rotation forest

Das

Tjahjadi

2016

Pattern Recognition Letters

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This paper proposes a gait recognition method which is invariant to maximum number of challenging factors of gait recognition mainly unpredictable variation in clothing and carrying conditions. The method introduces an averaged gait key-phase image (AGKI) which is computed by averaging each of the five key-phases of the gait periods of a gait sequence. It analyses the AGKIs using high-pass and low-pass Gaussian filters, each at three cut-off frequencies to achieve robustness against unpredictable variation in clothing and carrying conditions in addition to other covariate factors, e.g., walking speed, segmentation noise, shadows under feet and change in hair style and ground surface. The optimal cut-off frequencies of the Gaussian filters are determined based on an analysis of the focus values of filtered human subject's silhouettes. The method applies rotation forest ensemble learning recognition to enhance both individual accuracy and diversity within the ensemble for improved identification rate. Extensive experiments on public datasets demonstrate the efficacy of the proposed method.

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Section: Related Workmentioning

confidence: 99%

Section: Ou-isir Treadmill Gait Datasetmentioning

confidence: 99%

“…Unlike the method in [13], the goal of our method is to demonstrate robustness against unpredictable variation in clothing.…”

Section: Ou-isir Treadmill Gait Datasetmentioning

confidence: 99%

Section: Ou-isir Treadmill Gait Datasetmentioning

confidence: 99%

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Clothing and carrying condition invariant gait recognition based on rotation forest

Das

Tjahjadi

2016

Pattern Recognition Letters

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“…Since there are numerous covariates, it is challenging to create the appropriate gait template for robust gait recognition. The method in [16] applies part-based strategy to adaptively assign more weight to the unaffected body parts and less weight to the affected body parts to achieve insensitiveness to clothing variation. However, it is unrealistic to train the model with all known clothing types as attempted in [16].…”

Section: Introductionmentioning

confidence: 99%

Robust view-invariant multiscale gait recognition

Das

Tjahjadi

2015

Pattern Recognition

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The paper proposes a two-phase view-invariant multiscale gait recognition method (VI-MGR) which is robust to variation in clothing and presence of a carried item. In phase 1, VI-MGR uses the entropy of the limb region of a gait energy image (GEI) to determine the matching gallery view of the probe using 2-dimensional principal component analysis and Euclidean distance classifier. In phase 2, the probe subject is compared with the matching view of the gallery subjects using multiscale shape analysis. In this phase, VI-MGR applies Gaussian filter to a GEI to generate a multiscale gait image for gradually highlighting the subject's inner shape characteristics to achieve insensitiveness to boundary shape alterations due to carrying conditions and clothing variation. A weighted random subspace learning based classification is used to exploit the high dimensionality of the feature space for improved identification by avoiding overlearning. Experimental analyses on public datasets demonstrate the efficacy of VI-MGR.

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Gait Recognition: Databases, Representations, and Applications

Makihara

Matovski

Nixon

et al. 2015

Wiley Encyclopedia of Electrical and Electronics Engineering

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There has been considerable progress in automatic recognition of people by the way they walk since its inception almost 20 years ago: there is now a plethora of technique and data which continue to show that a person's walking is indeed unique. Gait recognition is a behavioural biometric which is available even at a distance from a camera when other biometrics may be occluded, obscured or suffering from insufficient image resolution (e.g. a blurred face image or a face image occluded by mask). Since gait recognition does not require subject cooperation due to its non-invasive capturing process, it is expected to be applied for criminal investigation from CCTV footages in public and private spaces. This article introduces current progress, a research background, and basic approaches for gait recognition in the first three sections, and two important aspects of gait recognition, the gait databases and gait feature representations are described in the following sections.Publicly available gait databases are essential for benchmarking individual approaches, and such databases should contain a sufficient number of subjects as well as covariate factors to realize statistically reliable performance evaluation and also robust gait recognition. Gait recognition researchers have therefore built such useful gait databases which incorporate subject diversities and/or rich covariate factors.Gait feature representation is also an important aspect for effective and efficient gait recognition. We describe the two main approaches to representation: model-free (appearance-based) approaches and model-based approaches. In particular, silhouette-based model-free approaches predominate in recent studies and many have been proposed and are described in detail.Performance evaluation results of such recent gait feature representations on two of the publicly available gait databases are reported: USF Human ID with rich covariate factors such as views, surface, bag, shoes, time elapse; and OU-ISIR LP with more than 4,000 subjects. Since gait recognition is suitable for criminal investigation applications of the gait recognition to forensics are addressed with real criminal cases in the application section. Finally, several open problems of the gait recognition are discussed to show future research avenues of the gait recognition.

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Clothing-invariant gait identification using part-based clothing categorization and adaptive weight control

Cited by 149 publications

References 19 publications

Clothing and carrying condition invariant gait recognition based on rotation forest

Clothing and carrying condition invariant gait recognition based on rotation forest

Robust view-invariant multiscale gait recognition

Gait Recognition: Databases, Representations, and Applications

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