Emotion Embedded Pose GenerationOpen Website

2020 (modified: 09 Nov 2022)ECCV Workshops (1) 2020Readers: Everyone
Abstract: Body poses are a rich source of information in the field of sentiment analysis and they complement existing facial emotion recognition tasks by adding significant value especially when faces are not easily available. CCTV recordings and other non-human interfaces are applications where capturing facial expression is challenging and these interfaces can benefit from body pose emotion recognition to conduct context analysis. Another roadblock in this direction is the limited availability of collected and curated datasets of body poses with emotion labels. Addressing these issues, we propose two end-to-end pipelines to generate emotion conditioned human poses corresponding to specific emotion labels. An auxiliary conditional GAN network is presented for pose images and pose skeleton pipelines. The generated images improved emotion classification accuracy by an average of 5.40% across 4 different networks compared to images that were traditionally augmented. Additionally, through image and skeletal augmentation, we achieve state-of-the-art emotion classification results for the BEAST dataset.
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