Kernel Fisher Discriminant Analysis for Natural Gait Cycle Based Gait Recognition


Jun Huang, Xiuhui Wang, Jun Wang, Journal of Information Processing Systems Vol. 15, No. 4, pp. 957-966, Aug. 2019  

10.3745/JIPS.02.0115
Keywords: Gait Energy Image, Gait Recognition, Kernel Fisher Discriminant Analysis, Natural Gait Cycle
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Abstract

This paper studies a novel approach to natural gait cycles based gait recognition via kernel Fisher discriminant analysis (KFDA), which can effectively calculate the features from gait sequences and accelerate the recognition process. The proposed approach firstly extracts the gait silhouettes through moving object detection and segmentation from each gait videos. Secondly, gait energy images (GEIs) are calculated for each gait videos, and used as gait features. Thirdly, KFDA method is used to refine the extracted gait features, and low-dimensional feature vectors for each gait videos can be got. The last is the nearest neighbor classifier is applied to classify. The proposed method is evaluated on the CASIA and USF gait databases, and the results show that our proposed algorithm can get better recognition effect than other existing algorithms.


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Cite this article
[APA Style]
Jun Huang, Xiuhui Wang, & Jun Wang (2019). Kernel Fisher Discriminant Analysis for Natural Gait Cycle Based Gait Recognition. Journal of Information Processing Systems, 15(4), 957-966. DOI: 10.3745/JIPS.02.0115.

[IEEE Style]
J. Huang, X. Wang and J. Wang, "Kernel Fisher Discriminant Analysis for Natural Gait Cycle Based Gait Recognition," Journal of Information Processing Systems, vol. 15, no. 4, pp. 957-966, 2019. DOI: 10.3745/JIPS.02.0115.

[ACM Style]
Jun Huang, Xiuhui Wang, and Jun Wang. 2019. Kernel Fisher Discriminant Analysis for Natural Gait Cycle Based Gait Recognition. Journal of Information Processing Systems, 15, 4, (2019), 957-966. DOI: 10.3745/JIPS.02.0115.