Abstract
This paper presents a new method for automatic gait recognition using independent component analysis (ICA). Firstly, a simple background subtraction algorithm is introduced to segment the moving figures accurately and to achieve binary silhouettes. Secondly, these 2D binary silhouettes are converted into associated sequences of 1D signals and ICA is applied to get the independent components of each 2D binary silhouettes. For the sake of reducing computation cost, a fast and robust fixed-point algorithm named FastICA is adopted. A criterion that not all ICs are useful for recognition is demonstrated and a method of IC selection is put forward. Lastly, the nearest neighbor (NN) classifier for recognition is chosen. This algorithm is tested on small MUD gait database and the NLPR gait database and experimental results show that our method has encouraging recognition accuracy.
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Lu, J., Zhang, E., Zhang, Z., Xue, Y. (2005). Gait Recognition Using Independent Component Analysis. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3497. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427445_29
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DOI: https://doi.org/10.1007/11427445_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25913-8
Online ISBN: 978-3-540-32067-8
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