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- W3200615053 abstract "This paper presents a novel data augmentation method to improve a walk-based person identification system. The proposed algorithm is based on trainable deep learning models, that are able to model the gait cycle of individual participants and generate perturbed augmented signals. In this study generative model involving two layers of Long Short-Term Memory (LSTM) and Mixture Density Network (MDN) was implemented." @default.
- W3200615053 created "2021-09-27" @default.
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- W3200615053 date "2021-01-01" @default.
- W3200615053 modified "2023-09-23" @default.
- W3200615053 title "Augmentation of Gait Cycles Using LSTM-MDN Networks in Person Identification System" @default.
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- W3200615053 doi "https://doi.org/10.1007/978-3-030-84340-3_4" @default.
- W3200615053 hasPublicationYear "2021" @default.
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