P329 - BIOSIG 2022 - Proceedings of the 21st International Conference of the Biometrics Special Interest Group

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Incorporation of Extra Pseudo Labels for CNN-based Gait Recognition
(BIOSIG 2022, 2022) Daigo Muramatsu, Kousuke Moriwaki
CNN is a major model used for image-based recognition tasks, including gait recognition, and many CNN-based network structures and/or learning frameworks have been proposed. Among them, we focus on approaches that use multiple labels for learning, typified by multi-task learning. These approaches are sometimes used to improve the accuracy of the main task by incorporating extra labels associated with sub-tasks. The incorporated labels for learning are usually selected from real tasks heuristically; for example, gender and/or age labels are incorporated together with subject identity labels.We take a different approach and consider a virtual task as a sub-task, and incorporate pseudo output labels together with labels associated with the main task and/or real task. In this paper, we focus on a gait-based person recognition task as the main task, and we discuss the effectiveness of virtual tasks with different pseudo labels for construction of a CNN-based gait feature extractor.