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dc.contributor.authorAnusha, R.
dc.contributor.authorJaidhar, C.D.
dc.date.accessioned2020-03-30T10:18:05Z-
dc.date.available2020-03-30T10:18:05Z-
dc.date.issued2019
dc.identifier.citation2019 International Conference on Data Science and Engineering, ICDSE 2019, 2019, Vol., , pp.71-76en_US
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/8109-
dc.description.abstractGait recognition is an expanding stream in biometrics, intended to recognize individuals through the investigation of their walking pattern. This pattern is obtained from a distance, without the active participation of the people. One of the difficulties of the appearance-based gait approach is to enhance the performance of frontal gait recognition, as it carries less spatial and temporal data when compared with other view variations. As a result, to increase the performance of the frontal gait recognition, this paper presents a method which uses two-step procedure; the Hierarchical centroid Shape descriptor (HCSD) and the similarity measurement. The proposed method was assessed on the broadly used CASIA A, CASIA B, and CMU MoBo gait databases. The experimental outcomes showed that the proposed method gave promising results and outperforms certain state-of-the-art methods in terms of recognition performance. � 2019 IEEE.en_US
dc.titleFrontal Gait Recognition based on Hierarchical Centroid Shape Descriptor and Similarity Measurementen_US
dc.typeBook chapteren_US
Appears in Collections:2. Conference Papers

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