Multi-task learning for gait-based identity recognition and emotion recognition using attention enhanced temporal graph convolutional network
作者：
Highlights：
• To our knowledge, this is the first work to treat identity recognition and emotion recognition as related tasks for jointly learning. We propose a multi-task learning architecture for gait-related recognition problems and achieve better performances by sharing knowledge.
• We propose a novel AT-GCN network for gait skeleton sequences, which can effectively capture discriminative spatiotemporal gait features. The attention mechanism is employed to enhance the expressive capability for achieving higher performance.
• We present a new dataset of human gaits (EMOGAIT), which consists of 1, 440 real-world gait videos annotated with identity labels and emotion labels. The proposed model achieves state-of-the-art results on both EMOGAIT dataset and TUMGAID dataset.
摘要
•To our knowledge, this is the first work to treat identity recognition and emotion recognition as related tasks for jointly learning. We propose a multi-task learning architecture for gait-related recognition problems and achieve better performances by sharing knowledge.•We propose a novel AT-GCN network for gait skeleton sequences, which can effectively capture discriminative spatiotemporal gait features. The attention mechanism is employed to enhance the expressive capability for achieving higher performance.•We present a new dataset of human gaits (EMOGAIT), which consists of 1, 440 real-world gait videos annotated with identity labels and emotion labels. The proposed model achieves state-of-the-art results on both EMOGAIT dataset and TUMGAID dataset.
论文关键词：Gait recognition,Gait emotion recognition,Graph convolutional network,Spatial-temporal attention GCN,Multi-task learning network
论文评审过程：Received 7 June 2020, Revised 15 September 2020, Accepted 27 January 2021, Available online 2 February 2021, Version of Record 12 February 2021.
论文官网地址：https://doi.org/10.1016/j.patcog.2021.107868
