Enhanced active color image for gait recognition

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Abstract

Active Energy Image (AEI) is an efficient template for gait recognition. However, the AEI is short of the temporal information. In this paper, we present a novel gait template, named Enhanced Active Color Image (EACI). The EACI is extract the difference of two interval in each gait frame, followed by calculating the width of that difference image and then mapping into RGB space with the ratio, describing the relative position, and composition them to a single EACI. To prove the validity of the EACI, we employ experiments on the USF HUMANID database. Experiment result shows that our EACI describes the dynamic, static and temporal information better. Compared with other published gait recognition approaches, we achieve competitive performance in gait recognition.

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Shang, Y., Song, Y., & Zhang, Y. (2016). Enhanced active color image for gait recognition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9967 LNCS, pp. 462–470). Springer Verlag. https://doi.org/10.1007/978-3-319-46654-5_51

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