Motion-based recognition of people in EigenGait space

BenAbdelkader, Chiraz; Cutler, Ross; Davis, Larry S.

doi:10.1109/afgr.2002.1004165

Cited by 130 publications

(108 citation statements)

References 15 publications

Supporting

Mentioning

107

Contrasting

Order By: Relevance

“…Noisy segmentation results bring about great impact on feature training and extraction, but it is less clear how much the silhouette type affects the recognition performance. As a whole, our method is comparable to the existing approaches in recognition accuracy, but far superior in observed execution times, the average is 1.6475min/seq, it outperforms [17][19] [20][21] [22]. Because data of gait database is huge, so in process of gait sequences, this is an advantage.…”

Section: Fig 2 Images From Usf Databasementioning

confidence: 58%

“…Results show our algorithm can deal with the speed change in gait efficiently. [11] 100 76 UMD [23,24] 72 32 UMD [7] 72 12 Georgia Tech.…”

Section: Recognition Results In Cmu Mobo Databasementioning

confidence: 99%

“…Gait recognition is used to signify the identification of individuals in image sequences 'by the way they walk' [7]. From a surveillance perspective, gait recognition is an attractive modality because it may be performed at a distance, surreptitiously.…”

Section: Introductionmentioning

confidence: 99%

“…So gait, as a biometric, has some advantages over other biometrics. Now, there are many methods to contribute to the gait analysis, for example model-based [3,12,13], appearance-based [14,15,16,17]. In the image sequences, translation and scale of walking people often exist, and moments are an efficient tool to deal with them.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Combining Wavelet Velocity Moments and Reflective Symmetry for Gait Recognition

Zhao

2005

Advances in Biometric Person Authentication

View full text Add to dashboard Cite

Abstract. Gait is a biometric feature and gait recognition has become a challenging problem in computer vision. New wavelet velocity moments have been developed to describe and recognize gait. Wavelet moments are translation, scale and rotation invariant. Wavelet analysis has the trait of multi-resolution analysis, which strengthens the analysis ability to image subtle feature. According with the psychological studies, reflective symmetry features are introduced to help recognition. Combination of wavelet velocity moments and reflective symmetry not only has the characteristic of wavelet moments, but also reflects the person's walking habit of symmetry. Experiments on two databases show the proposed combined features of wavelet velocity moments and reflective symmetry are efficient to describe gait.

show abstract

Section: Fig 2 Images From Usf Databasementioning

confidence: 58%

“…Results show our algorithm can deal with the speed change in gait efficiently. [11] 100 76 UMD [23,24] 72 32 UMD [7] 72 12 Georgia Tech.…”

Section: Recognition Results In Cmu Mobo Databasementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Combining Wavelet Velocity Moments and Reflective Symmetry for Gait Recognition

Zhao

2005

Advances in Biometric Person Authentication

View full text Add to dashboard Cite

show abstract

“…Model based approaches [6], [7] aim to derive the movement of the torso and/or the legs, recovering explicit features describing gait dynamics of joint angles. On the other hand, model-free approaches are mainly silhouette-based.…”

Section: Introductionmentioning

confidence: 99%

Human Perambulation as a Self Calibrating Biometric

Goffredo

Spencer

Pearce

et al.

Analysis and Modeling of Faces and Gestures

View full text Add to dashboard Cite

Abstract. This paper introduces a novel method of single camera gait reconstruction which is independent of the walking direction and of the camera parameters. Recognizing people by gait has unique advantages with respect to other biometric techniques: the identification of the walking subject is completely unobtrusive and the identification can be achieved at distance. Recently much research has been conducted into the recognition of frontoparallel gait. The proposed method relies on the very nature of walking to achieve the independence from walking direction. Three major assumptions have been done: human gait is cyclic; the distances between the bone joints are invariant during the execution of the movement; and the articulated leg motion is approximately planar, since almost all of the perceived motion is contained within a single limb swing plane. The method has been tested on several subjects walking freely along six different directions in a small enclosed area. The results show that recognition can be achieved without calibration and without dependence on view direction. The obtained results are particularly encouraging for future system development and for its application in real surveillance scenarios.

show abstract

Gait Recognition: Databases, Representations, and Applications

Makihara

Matovski

Nixon

et al. 2015

Wiley Encyclopedia of Electrical and Electronics Engineering

View full text Add to dashboard Cite

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.

show abstract

Motion-based recognition of people in EigenGait space

Cited by 130 publications

References 15 publications

Combining Wavelet Velocity Moments and Reflective Symmetry for Gait Recognition

Combining Wavelet Velocity Moments and Reflective Symmetry for Gait Recognition

Human Perambulation as a Self Calibrating Biometric

Gait Recognition: Databases, Representations, and Applications

Contact Info

Product

Resources

About