Gait based recognition via fusing information from Euclidean and Riemannian manifolds
| Autor: | George Economou, Dimitris Kastaniotis, Spiros Fotopoulos, Ilias Theodorakopoulos |
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| Rok vydání: | 2016 |
| Předmět: |
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business.industry ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 020206 networking & telecommunications Pattern recognition 02 engineering and technology Data mapping ComputingMethodologies_PATTERNRECOGNITION Gait (human) Artificial Intelligence Signal Processing Euclidean geometry 0202 electrical engineering electronic engineering information engineering Feature (machine learning) 020201 artificial intelligence & image processing Computer vision Computer Vision and Pattern Recognition Artificial intelligence Representation (mathematics) business Focus (optics) Software Reproducing kernel Hilbert space Mathematics |
| Zdroj: | Pattern Recognition Letters. 84:245-251 |
| ISSN: | 0167-8655 |
| DOI: | 10.1016/j.patrec.2016.10.012 |
| Popis: | Pose-based gait recognition using Euclidean and Riemannian feature representations.Euclidean representation is based on a residual aggregation method.Riemannian is based on the covariance representation of a sequence.A new publicly available dataset acquired using Kinect 2 is presented.Fusion and Classification is performed via SRC in RKHS. Gait is a particular periodical type of human motion with several unique characteristics for every person. In this work we focus on the problem of pose based gait recognition. The contribution of the proposed work is threefold. First we represent every gait sequence according to both the deviation of the poses from an appropriate global model, as well as the intra-sequence pose variability. Secondly, we propose a method which allows us to fuse information from feature representations from both Euclidean and Riemannian spaces by mapping data in a Reproducing Kernel Hilbert Space (RKHS). Classification is then performed using a kernelized version of the SRC algorithm. Third we present a new publicly available dataset for pose based gait recognition captured with Kinect V2. Experimental evaluation reveals state-of-the-art performance in both recognition and verification tasks and a capacity for real-time operation. |
| Databáze: | OpenAIRE |
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