Gait Analysis for Human Identification

Kale, Amit; Cuntoor, Naresh P.; Yegnanarayana, B.; Rajagopalan, A. N.; Chellappa, Rama

doi:10.1007/3-540-44887-x_82

Cited by 176 publications

(136 citation statements)

References 6 publications

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“…5 together with the overall view and clearly all other biometric information is missing and only the subject's gait can be perceived. Recognition rates similar to those on other data have been reported, some of the example rates here are early [26,45,48,49,52]. Some of the peak correct classification rates (CCRs) for subjects imaged from a different viewpoint and from the same viewpoint but wearing other shoes are given in Table 1.…”

Section: : Example Gait Challenge Resultssupporting

confidence: 84%

“…The innate performance factors concern the effect of covariates on recognition performance, but with deeper analysis to determine data pertinent to recognition [53] with a view to refining technique development. The practical performance factors concern the intrinsic effects, such as the consequences of speed [54] and load, and extrinsic effects which especially include variation in viewpoint [55,52] and 3D analysis [57] with synchronization driving the need for novel (temporal) view synthesis [58]. There is natural means to handle difficulty in image acquisition by using infrared [59], and some of the recent developments in radar might also be used to good effect.…”

Section: Future Workmentioning

confidence: 99%

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Advances in automatic gait recognition

Nixon

Carter

Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings.

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Section: : Example Gait Challenge Resultssupporting

confidence: 84%

Section: Future Workmentioning

confidence: 99%

Advances in automatic gait recognition

Nixon

Carter

Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings.

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“…One of the earliest view-dependent approaches can be seen in [25], where the outer contour of the binarized silhouette from a lateral view is used to build a descriptor which contains both structural features and dynamic aspects of gait. The contours of silhouettes have also been used directly [40,18], and through their Fourier descriptors [33,29].…”

Section: View-dependent Approachesmentioning

confidence: 99%

Entropy volumes for viewpoint-independent gait recognition

López-Fernández

Madrid-Cuevas

Carmona-Poyato

et al. 2015

Machine Vision and Applications

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Gait as biometrics has been widely used for human identification. However, direction changes cause difficulties for most of the gait recognition systems, due to appearance changes. This study presents an efficient multi-view gait recognition method that allows curved trajectories on completely unconstrained paths for indoor environments. Our method is based on volumetric reconstructions of humans, aligned along their way. A new gait descriptor, termed as Gait Entropy Volume (GEnV), is also proposed. GEnV focuses on capturing 3D dynamical information of walking humans through the concept of entropy. Our approach does not require the sequence to be split into gait cycles. A GEnV based signature is computed on the basis of the previous 3D gait volumes. Each signature is classified by a Support Vector Machine, and a majority voting policy is used to smooth and reinforce the classifications results. The proposed approach is experimentally validated on the "AVA Multi-View Gait Dataset (AVAMVG)" and on the "Kyushu University 4D Gait Database (KY4D)". The results show that this new approach achieves promising results in the problem of gait recognition on unconstrained paths.

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“…In [12], we explored the use of the width vector of the outer contour of the binarized silhouette as a feature for gait representation. Matching two sequences of width vectors was performed using the Dynamic Time Warping (DTW).…”

Section: Gait-based Person Identificationmentioning

confidence: 99%

Pattern Recognition in Video

Chellappa

Veeraraghavan

Aggarwal

2005

Lecture Notes in Computer Science

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Abstract. Images constitute data that live in a very high dimensional space, typically of the order of hundred thousand dimensions. Drawing inferences from correlated data of such high dimensions often becomes intractable. Therefore traditionally several of these problems like face recognition, object recognition, scene understanding etc. have been approached using techniques in pattern recognition. Such methods in conjunction with methods for dimensionality reduction have been highly popular and successful in tackling several image processing tasks. Of late, the advent of cheap, high quality video cameras has generated new interests in extending still image-based recognition methodologies to video sequences. The added temporal dimension in these videos makes problems like face and gait-based human recognition, event detection, activity recognition addressable. Our research has focussed on solving several of these problems through a pattern recognition approach. Of course, in video streams patterns refer to both patterns in the spatial structure of image intensities around interest points and temporal patterns that arise either due to camera motion or object motion. In this paper, we discuss the applications of pattern recognition in video to problems like face and gait-based human recognition, behavior classification, activity recognition and activity based person identification.

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Gait Analysis for Human Identification

Cited by 176 publications

References 6 publications

Advances in automatic gait recognition

Advances in automatic gait recognition

Entropy volumes for viewpoint-independent gait recognition

Pattern Recognition in Video

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