Model-based human gait recognition using leg and arm movements

Tafazzoli, Faezeh; Safabakhsh, Reza

doi:10.1016/j.engappai.2010.07.004

Cited by 145 publications

(73 citation statements)

References 25 publications

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“…Model-based methods (e.g., [17,23,9]) use a structural model to measure time-varying gait parameters, e.g., gait period, stance width and stride length, and a motion model to analyse the kinematical and dynamical motion parameters of the subject, e.g., rotation patterns of hip and thigh, and joint angle trajectories, to obtain gait signatures. The modelfree gait recognition methods in [4,3,27] analyse the dynamic and/or static gait characteristics of silhouettes or the extreme outer boundary of silhouettes, i.e., contours of a gait sequence.…”

Section: Related Workmentioning

confidence: 99%

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%

Clothing and carrying condition invariant gait recognition based on rotation forest

Das

Tjahjadi

2016

Pattern Recognition Letters

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“…C. Senanyake et al [10] used thigh, shank, foot, hip, ankle and knee body joint for normal gait sequences achieved better result on body joint. Faezeh Tafazzoli [11] extracted the gait motion of leg and arm. They computed the rotation variation of hip, knee and arm of silhouettes images and found better result with arm as compare to without arm variation.…”

Section: Related Workmentioning

confidence: 99%

Gait Recognition with Fuzzy Classification Using Shoulder Body Joint

Bharti¹,

Gupta²

2013

IJFCC

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Abstract-In this paper, the adopted methodology based on intersection with fuzzy classification. Consider two human body joint i.e. shoulder and feet. Feet body joint is separated in to two component toe and heel of the left and right leg. Two triangles are formed on joining the shoulder and feet (toe and heel). The intersecting points are computed on CASIA database. Fuzzy classification yield enhanced recognition rate.

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“…Model-based methods (e.g., [8,9,10,11]) characterise a human subject using a structural model to measure time-varying gait parameters, e.g., gait period, stance width and stride length, and a motion model to analyse the kinematic and dynamical motion parameters of the subject, e.g., rotation patterns of hip and thigh, joint angle trajectories and orientation change of limbs.…”

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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Model-based human gait recognition using leg and arm movements

Cited by 145 publications

References 25 publications

Clothing and carrying condition invariant gait recognition based on rotation forest

Clothing and carrying condition invariant gait recognition based on rotation forest

Gait Recognition with Fuzzy Classification Using Shoulder Body Joint

Robust view-invariant multiscale gait recognition

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