Abstract
The scope of this article is selection of individual gait features of video recordings data. The gait sequences are considered to be the 3rd-order tensors and their features are extracted by Multilinear Principal Component Analysis. Obtained gait descriptors are reduced by the supervised selection with greedy hill climbing and genetics search methods. To evaluate the explored individual feature sets, classification is carried out and CFS correlation based measure is utilized. The experimental phase is based on the CASIA Gait Database ’dataset A’. The obtained results are promising. Feature selection gives much more compact gait descriptors and causes significant improvement of human identification.
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Josiński, H., Michalczuk, A., Polański, A., Świtoński, A., Wojciechowski, K. (2013). Selection of Individual Gait Features Extracted by MPCA Applied to Video Recordings Data. In: Nawrat, A., Kuś, Z. (eds) Vision Based Systemsfor UAV Applications. Studies in Computational Intelligence, vol 481. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00369-6_17
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DOI: https://doi.org/10.1007/978-3-319-00369-6_17
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