A New Gait Representation for Human Identification: Mass Vector

Hong, Sungjun; Lee, Heesung; Nizami, Imran Fareed; Kim, Euntai

doi:10.1109/iciea.2007.4318491

Cited by 34 publications

(30 citation statements)

References 12 publications

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“…A large number of papers have been tested on this database, obtaining 92.5% recognition rate on the lateral view. Most of them are statistical based, including the approach of Yang et al [25], the mass vector method of Hong et al [26], the fusion methodology of Nizami et al [27], the 2-D polar-plane silhouette description of Chen and Gao [23], and the silhouette spatiotemporal spectrum of Lam et al [28].…”

Section: A Multiview Data Setsmentioning

confidence: 99%

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Self-Calibrating View-Invariant Gait Biometrics

Goffredo

Bouchrika

Carter

et al. 2010

IEEE Trans. Syst., Man, Cybern. B

185

111

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Abstract-We present a new method for viewpoint independent gait biometrics. The system relies on a single camera, does not require camera calibration, and works with a wide range of camera views. This is achieved by a formulation where the gait is self-calibrating. These properties make the proposed method particularly suitable for identification by gait, where the advantages of completely unobtrusiveness, remoteness, and covertness of the biometric system preclude the availability of camera information and specific walking directions. The approach has been assessed for feature extraction and recognition capabilities on the SOTON gait database and then evaluated on a multiview database to establish recognition capability with respect to view invariance. Moreover, tests on the multiview CASIA-B database, composed of more than 2270 video sequences with 65 different subjects walking freely along different walking directions, have been performed. The obtained results show that human identification by gait can be achieved without any knowledge of internal or external camera parameters with a mean correct classification rate of 73.6% across all views using purely dynamic gait features. The performance of the proposed method is particularly encouraging for application in surveillance scenarios.Index Terms-Gait biometrics, human identification, view invariant.

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Section: A Multiview Data Setsmentioning

confidence: 99%

“…The pose-free approaches extract some gait parameters which are independent from the camera point of view [7], [22], [23], [26], [31], [32]. On the other hand, the pose-based method aims to synthesize the lateral view of the human body from an arbitrary viewpoint [33]- [35].…”

Section: B View-invariant Literaturementioning

confidence: 99%

Self-Calibrating View-Invariant Gait Biometrics

Goffredo

Bouchrika

Carter

et al. 2010

IEEE Trans. Syst., Man, Cybern. B

185

111

View full text Add to dashboard Cite

show abstract

“…Yang et al [11] obtained the dynamic region in gait energy image (GEI) which reflects the walking manner of an individual, called enhanced GEI. In [12], Hong et al extracted a gait feature called mass vector from the number of pixels with nonzero value in a given row of the binary silhouette. They used dynamic time-warping approach to match gait templates formed by the mass vectors.…”

Section: Introductionmentioning

confidence: 99%

Biometric Authorization System Using Gait Biometry

Sudha¹,

Bhavani²

2011

IJCSEA

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“…On the other hands, we proposed the projection of a silhouette image as a gait feature called the mass vector in our previous work [12]. The mass vector is defined as the number of nonzero pixels in row of a silhouette image.…”

Section: Gait Representationmentioning

confidence: 99%

“…Since most biometrics such as fingerprint, face, and iris are restricted to controlled environment, gait can be alternately used in situations where other biometrics might not be applicable. Research related to human gait includes gait modeling [1], gait recognition based on model [2][3][4][5] or appearance [6][7][8][9][10][11][12][13], gait feature analysis [4,14], silhouette extraction and refinement [3,7,15], etc. It has great potential in human identification and visual surveillance systems.…”

Section: Introductionmentioning

confidence: 99%

A New Automatic Gait Cycle Partitioning Method and Its Application to Human Identification

Hong

Kim

2017

IJFIS

Self Cite

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Gait cycle partitioning is very important prior to human gait analysis such as gait modeling, gait recognition, gait feature analysis, etc. In this paper, we propose a new automatic gait cycle partitioning method based on two kinds of simple gait representation. Then, the propose method is applied to find keyframe corresponding the rest position for gait-based human identification system. To demonstrate the validity of the proposed method, the CASIA gait dataset A and the SOTON gait database are used to evaluate the recognition performance of the gait recognition system identifying subjects using the decision level fusion based on majority voting.

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A New Gait Representation for Human Identification: Mass Vector

Cited by 34 publications

References 12 publications

Self-Calibrating View-Invariant Gait Biometrics

Self-Calibrating View-Invariant Gait Biometrics

Biometric Authorization System Using Gait Biometry

A New Automatic Gait Cycle Partitioning Method and Its Application to Human Identification

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