FEAFA+: an extended well-annotated dataset for facial expression analysis and 3D facial animation

Wei Gan; Jian Xue; Ke Lu; Yanfu Yan; Pengcheng Gao; Jiayi Lyu

doi:10.1117/12.2643588

12 October 2022 FEAFA+: an extended well-annotated dataset for facial expression analysis and 3D facial animation

Wei Gan, Jian Xue, Ke Lu, Yanfu Yan, Pengcheng Gao, Jiayi Lyu

Proceedings Volume 12342, Fourteenth International Conference on Digital Image Processing (ICDIP 2022); 1234211 (2022) https://doi.org/10.1117/12.2643588
Event: Fourteenth International Conference on Digital Image Processing (ICDIP 2022), 2022, Wuhan, China

Abstract

The purpose of this paper is to present a dataset for facial expression analysis and facial animation. Nearly all existing Facial Action Coding System-based datasets that include facial action unit (AU) intensity information annotate the intensity values hierarchically using A–E levels. However, facial expressions change continuously and shift smoothly from one state to another. Therefore, it is more effective to regress the intensity value of local facial AUs to represent whole facial expression changes, particularly in the fields of expression transfer and facial animation. We introduce an extension of FEAFA in combination with the relabeled DISFA database, which is available at http://www.iiplab.net/feafa+/ now. Extended FEAFA (FEAFA+) includes 154 video sequences from FEAFA and DISFA, with a total of 230,184 frames being manually annotated on floating-point intensity values of 24 redefined AUs using the Expression Quantitative Tool. We list crude numerical results for posed and spontaneous subsets and provide a baseline comparison for the AU intensity regression task.

Citation Download Citation

Wei Gan, Jian Xue, Ke Lu, Yanfu Yan, Pengcheng Gao, and Jiayi Lyu "FEAFA+: an extended well-annotated dataset for facial expression analysis and 3D facial animation", Proc. SPIE 12342, Fourteenth International Conference on Digital Image Processing (ICDIP 2022), 1234211 (12 October 2022); https://doi.org/10.1117/12.2643588

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KEYWORDS

Video

Reliability

Databases

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Facial recognition systems

Digital image processing

Face image reconstruction

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