Gait recognition for randomwalking patterns and variable body postures

Huang, Xiaxi; Boulgouris, Nikolaos V.

doi:10.1109/icassp.2010.5495467

Cited by 6 publications

(3 citation statements)

References 11 publications

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“…The human walking manner, termed gait, has been well known to have the ability to be used for identification [43]. The biometric system or technology has become famous because of many application needs for human recognition in surveillance system.…”

Section: Introductionmentioning

confidence: 99%

Human Identification Using Sensors Data Based on 3D Gait Area

Ali

Zhou

et al. 2014

2014 International Conference on Cyberworlds

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Surveillance means is a system that monitors the behavior, activities, or other changing information; usually it is used to recognize the people for the purpose of security issues in society. This paper aims to propose a novel approach based on sensor data acquired by an optical system for the purpose of human identification. Three joints of the human body, such as the hip, knee, and ankle joint have been selected by the amount of gait movement in this algorithm. By extracting suitable 3D static and dynamic joints feature from data. The method applies the PBC (Parametric Bézier Curve) technique on the extracted features in order to derive the strong correlation between joint movements. The curve control points are used to construct the triangles of each walking pose of human. The geometric function and statistical methods on the triangles are used here to compute the gait signature and analysis of this signature and find the maximum unique relationship among the human gait during the walk, and use it as to classify the human identification. The experimental results demonstrate that this method is more accurate and reliable.

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Section: Introductionmentioning

confidence: 99%

Human Identification Using Sensors Data Based on 3D Gait Area

Ali

Zhou

et al. 2014

2014 International Conference on Cyberworlds

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“…Moreover in [20] there is a presentation of a new gait recognition method that does not presume the existence of strict lab conditions for its operation.…”

Section: Recent Workmentioning

confidence: 99%

Gait Recognition Based on Invariant Leg Classification Using a Neuro-Fuzzy Algorithm as the Fusion Method

Yazdi

Fariman

Roohi

2012

ISRN Artificial Intelligence

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This paper presents a human gait recognition algorithm based on a leg gesture separation. Main innovation in this paper is gait recognition using leg gesture classification which is invariant to covariate conditions during walking sequence and just focuses on underbody motions and a neuro-fuzzy combiner classifier (NFCC) which derives a high precision recognition system. At the end, performance of the proposed algorithm has been validated by using the HumanID Gait Challenge data set (HGCD), the largest gait benchmarking data set with 122 objects with different realistic parameters including viewpoint, shoe, surface, carrying condition, and time. And it has been compared to recent algorithm of gait recognition.

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“…However, in this work, instead of traditional one-gait-cycle GEI, we use half-gait-cycle GEI (e.g., Fig. 3(c)) to increase the size of the training samples for more dynamic information, according to the fact that most people walk symmetrically in the first and second half of a gait cycle [8].…”

Section: Dynamic Feature Template Definitionmentioning

confidence: 99%

Gait recognition under carrying condition: a static dynamic fusion method

2012

SPIE Proceedings

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When an individual carries an object, such as a briefcase, conventional gait recognition algorithms based on average silhouette/Gait Energy Image (GEI) do not always perform well as the object carried may have the potential of being mistakenly regarded as a part of the human body. To solve such a problem, in this paper, instead of directly applying GEI to represent the gait information, we propose a novel dynamic feature template for classification. Based on this extracted dynamic information and some static feature templates (i.e., head part and trunk part), we cast gait recognition on the large USF (University of South Florida) database by adopting a static/dynamic fusion strategy. For the experiments involving carrying condition covariate, significant improvements are achieved when compared with other classic algorithms.

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Gait recognition for randomwalking patterns and variable body postures

Cited by 6 publications

References 11 publications

Human Identification Using Sensors Data Based on 3D Gait Area

Human Identification Using Sensors Data Based on 3D Gait Area

Gait Recognition Based on Invariant Leg Classification Using a Neuro-Fuzzy Algorithm as the Fusion Method

Gait recognition under carrying condition: a static dynamic fusion method

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