33 research outputs found

    Human gait recognition based on multiview gait sequences

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    Copyright © 2008 X. Huang and N. V. Boulgouris. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Most of the existing gait recognition methods rely on a single view, usually the side view, of the walking person. This paper investigates the case in which several views are available for gait recognition. It is shown that each view has unequal discrimination power and, therefore, should have unequal contribution in the recognition process. In order to exploit the availability of multiple views, several methods for the combination of the results that are obtained from the individual views are tested and evaluated. A novel approach for the combination of the results from several views is also proposed based on the relative importance of each view. The proposed approach generates superior results, compared to those obtained by using individual views or by using multiple views that are combined using other combination methods.European Commissio

    On Using Gait Biometrics to Enhance Face Pose Estimation

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    Many face biometrics systems use controlled environments where subjects are viewed directly facing the camera. This is less likely to occur in surveillance environments, so a process is required to handle the pose variation of the human head, change in illumination, and low frame rate of input image sequences. This has been achieved using scale invariant features and 3D models to determine the pose of the human subject. Then, a gait trajectory model is generated to obtain the correct the face region whilst handing the looming effect. In this way, we describe a new approach aimed to estimate accurate face pose. The contributions of this research include the construction of a 3D model for pose estimation from planar imagery and the first use of gait information to enhance the face pose estimation process

    2.5D multi-view gait recognition based on point cloud registration

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    This paper presents a method for modeling a 2.5-dimensional (2.5D) human body and extracting the gait features for identifying the human subject. To achieve view-invariant gait recognition, a multi-view synthesizing method based on point cloud registration (MVSM) to generate multi-view training galleries is proposed. The concept of a density and curvature-based Color Gait Curvature Image is introduced to map 2.5D data onto a 2D space to enable data dimension reduction by discrete cosine transform and 2D principle component analysis. Gait recognition is achieved via a 2.5D view-invariant gait recognition method based on point cloud registration. Experimental results on the in-house database captured by a Microsoft Kinect camera show a significant performance gain when using MVSM

    On using gait to enhance frontal face extraction

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    Visual surveillance finds increasing deployment formonitoring urban environments. Operators need to be able to determine identity from surveillance images and often use face recognition for this purpose. In surveillance environments, it is necessary to handle pose variation of the human head, low frame rate, and low resolution input images. We describe the first use of gait to enable face acquisition and recognition, by analysis of 3-D head motion and gait trajectory, with super-resolution analysis. We use region- and distance-based refinement of head pose estimation. We develop a direct mapping to relate the 2-D image with a 3-D model. In gait trajectory analysis, we model the looming effect so as to obtain the correct face region. Based on head position and the gait trajectory, we can reconstruct high-quality frontal face images which are demonstrated to be suitable for face recognition. The contributions of this research include the construction of a 3-D model for pose estimation from planar imagery and the first use of gait information to enhance the face extraction process allowing for deployment in surveillance scenario

    Gait Dynamics for Recognition and Classification

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    This paper describes a representation of the dynamics of human walking action for the purpose of person identification and classification by gait appearance. Our gait representation is based on simple features such as moments extracted from video silhouettes of human walking motion. We claim that our gait dynamics representation is rich enough for the task of recognition and classification. The use of our feature representation is demonstrated in the task of person recognition from video sequences of orthogonal views of people walking. We demonstrate the accuracy of recognition on gait video sequences collected over different days and times, and under varying lighting environments. In addition, preliminary results are shown on gender classification using our gait dynamics features

    Ambient Intelligence through Image Retrieval

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    An ambient intelligent environment needs dynamic enrollment of strangers without too much human intervention. For this purpose, we propose an entity recognition process based on images captured with low-cost but widespread webcams and easy-to-deploy image processing techniques. We find that the use of levels of confidence in recognition due to different techniques and context-based image retrieval improves the process

    Spatio-temporal alignment and hyperspherical radon transform for 3D gait recognition in multi-view environments

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    This paper presents a view-invariant approach to gait recognition in multi-camera scenarios exploiting a joint spatio-temporal data representation and analysis. First, multi-view information is employed to generate a 3D voxel reconstruction of the scene under study. The analyzed subject is tracked and its centroid and orientation allow recentering and aligning the volume associated to it, thus obtaining a representation invariant to translation, rotation and scaling. Temporal periodicity of the walking cycle is extracted to align the input data in the time domain. Finally, Hyperspherical Radon Transform is presented as an efficient tool to obtain features from spatio-temporal gait templates for classification purposes. Experimental results prove the validity and robustness of the proposed method for gait recognition tasks with several covariates.Postprint (published version

    Gait Identification Considering Body Tilt byWalking Direction Changes

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    Gait identification has recently gained attention as a method of identifying individuals at a distance. Thought most of the previous works mainly treated straight-walk sequences for simplicity, curved-walk sequences should be also treated considering situations where a person walks along a curved path or enters a building from a sidewalk. In such cases, person's body sometimes tilts by centrifugal force when walking directions change, and this body tilt considerably degrades gait silhouette and identification performance, especially for widely-used appearance-based approaches. Therefore, we propose a method of body-tilted silhouette correction based on centrifugal force estimation from walking trajectories. Then, gait identification process including gait feature extraction in the frequency domain and learning of a View Transformation Model (VTM) follows the silhouette correction. Experiments of gait identification for circular-walk sequences demonstrate the effectiveness of the proposed method

    The University of Southampton Multi-Biometric Tunnel and introducing a novel 3D gait dataset

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