Viewpoint-independent gait recognition through morphological descriptions of 3D human reconstructions*
Introduction
Research on human gait as a biometric feature for identification has received a lot of attention due to the apparent advantage that it can be applied discreetly on the observed individual without needing the active participation of the individual.
Previous studies on gait recognition have been classified into two categories: model-based and model-free approaches. The model-based methods extract gait features by fitting a model to input images, whereas model-free approaches characterize the human gait pattern by a compact representation, without having to develop any articulated model for feature extraction and having practical application even with low quality images where the color and texture information is lost.
In addition, regarding viewing angle, the previous work can be categorized into two approaches: view-dependent and view-independent approaches. The view-dependent approaches assume that the appearance will not change during walking. In such methods, a change in the appearance, caused by a view change, will adversely affect performance [1]. Fig. 1 shows the influence of a curved path on the silhouette appearance. As one of the advantages of gait as biometric is that it does not need the cooperation of the individual, the trajectory of motion cannot be restricted to just straight paths.
On the other hand, the use of volumetric information allows more information to be analyzed in contrast to methods which only compute gait descriptors from silhouettes or 2D images. This paper presents an efficient view-independent method to recognize people walking along unconstrained (curved and straight) trajectories. This approach focuses on capturing 3D morphological and structural information from volumetric reconstructions of walking humans, which are previously aligned along the way. The main contribution is that our method allows direction changes, achieving a good recognition rate on unconstrained paths.
Some potential applications of this work are access control in special or restricted areas (e.g. military bases, governmental facilities and laboratories) or smart video surveillance (e.g. bank offices) [2].
This article is organized as follows. Section 2 describes works related to the topic of gait recognition. Section 3 explains the details of the proposed algorithm and describes three new descriptors which obtain information from 3D occupancy volumes. An analysis of the proposed method and the performance is given in Section 4. Finally, we conclude this paper in Section 5.
Section snippets
Related work
The previous work can be categorized into two approaches: view-dependent and view-independent approaches. In the following we describe works related to both categories.
Proposed method
We propose a model-free approach to recognize walking humans independently of the viewpoint and regardless direction changes. Our approach focuses on capturing 3D morphological and structural information from the gait through volumetric reconstructions of the walking humans.
The use of volumetric information allows more information to be analyzed in contrast to other related works, which only compute gait descriptors from silhouettes, discarding an important part of the dynamical and structural
Experiments and discussion
In this section we start by describing the used datasets, and then we present the experimental results.
Conclusions
This paper has proposed a method to recognize walking humans independently of the viewpoint and regardless direction changes. The method focuses on capturing 3D morphological and structural information from volumetric reconstructions of the gait. The main contribution is that the method achieves a good recognition rate on completely unconstrained paths, allowing direction changes, in contrast to others view-independent approaches where the view change is restricted to a few angles. In our
Acknowledgments
This work has been developed with the support of the Research Projects called TIN2012-32952 and BROCA both financed by Science and Technology Ministry of Spain and FEDER.
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Person re-identification based on gait via Part View Transformation Model under variable covariate conditions
2021, Journal of Visual Communication and Image RepresentationCitation Excerpt :The person was recognized and his walking direction was estimated by comparing the gait features with those in the database. López-Fernández et al. [21,22] presented a rotation invariant gait descriptor based on 3D angular analysis of the subject’s movement for multi-view gait recognition on unconstrained paths. However, the need for multiple cameras and camera calibration limits the feasibility of this first category in real applications.
Biometrics: Going 3D
2022, SensorsRobust gait based human identification on incomplete and multi-view sequences
2021, Multimedia Tools and ApplicationsGait recognition and understanding based on hierarchical temporal memory using 3D gait semantic folding
2020, Sensors (Switzerland)
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This paper has been recommended for acceptance by Mark S. Nixon.
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