Abstract
Clothing, carrying conditions, and other intra-class variations, also referred as ”covariates”, affect the performance of gait recognition systems. This paper proposes a supervised feature extraction method which is able to select relevant features for human recognition to mitigates the impact of covariates and hence improve the recognition performance. The proposed method is evaluated using CASIA Gait Database (Dataset B) and the experimental results suggest that our method yields attractive results.
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Rida, I., Bouridane, A., Al Kork, S., Bremond, F. (2014). Gait Recognition Based on Modified Phase Only Correlation. In: Elmoataz, A., Lezoray, O., Nouboud, F., Mammass, D. (eds) Image and Signal Processing. ICISP 2014. Lecture Notes in Computer Science, vol 8509. Springer, Cham. https://doi.org/10.1007/978-3-319-07998-1_48
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DOI: https://doi.org/10.1007/978-3-319-07998-1_48
Publisher Name: Springer, Cham
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