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- W3026393948 abstract "Gender classification is in high demand because of its versatility. It can be used as a surveillance system for security. It can also be used to classify customers in retail establishments. In this work, we propose a new gender classification technique from a gait silhouette using observation angle-based GEIs. The system consists of two main parts: the observation angle classification model and the gender classification model. The observation angle classification generates 10 observation angle-based GEIs. The gender classification model uses angle-based GEIs to predict gender. The proposed methods perform well with freestyle walks which contain a viewpoint issue. The proposed model achieves 90.74% accuracy in the freestyle walk dataset (SIIT-CN-B) and 97.58% on the CASIA-B (fixed-direction walk) dataset. The proposed model outperforms the existing models in both freestyle and fixed-direction walk datasets." @default.
- W3026393948 created "2020-05-29" @default.
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- W3026393948 date "2019-11-01" @default.
- W3026393948 modified "2023-10-17" @default.
- W3026393948 title "Gender classification from gait silhouette using observation angle-based GEIs" @default.
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- W3026393948 doi "https://doi.org/10.1109/cis-ram47153.2019.9095797" @default.
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