Deep Impression: Audiovisual Deep Residual Networks for Multimodal Apparent Personality Trait Recognition

Yagmur Güçlütürk, Umut Güçlü, Marcel A. J. van Gerven, Rob van Lier

ECCV 2016 pp. 349-358

doi:10.1007/978-3-319-49409-8_28 /eccv/2016/gucluturk2016eccv-deep/

Abstract

Here, we develop an audiovisual deep residual network for multimodal apparent personality trait recognition. The network is trained end-to-end for predicting the Big Five personality traits of people from their videos. That is, the network does not require any feature engineering or visual analysis such as face detection, face landmark alignment or facial expression recognition. Recently, the network won the third place in the ChaLearn First Impressions Challenge with a test accuracy of 0.9109.

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Text

Güçlütürk et al. "Deep Impression: Audiovisual Deep Residual Networks for Multimodal Apparent Personality Trait Recognition." European Conference on Computer Vision, 2016. doi:10.1007/978-3-319-49409-8_28

Markdown

[Güçlütürk et al. "Deep Impression: Audiovisual Deep Residual Networks for Multimodal Apparent Personality Trait Recognition." European Conference on Computer Vision, 2016.](https://mlanthology.org/eccv/2016/gucluturk2016eccv-deep/) doi:10.1007/978-3-319-49409-8_28

BibTeX

@inproceedings{gucluturk2016eccv-deep,
  title     = {{Deep Impression: Audiovisual Deep Residual Networks for Multimodal Apparent Personality Trait Recognition}},
  author    = {Güçlütürk, Yagmur and Güçlü, Umut and van Gerven, Marcel A. J. and van Lier, Rob},
  booktitle = {European Conference on Computer Vision},
  year      = {2016},
  pages     = {349-358},
  doi       = {10.1007/978-3-319-49409-8_28},
  url       = {https://mlanthology.org/eccv/2016/gucluturk2016eccv-deep/}
}