Neuromorphic Event-based Facial Expression Recognition

Lorenzo Berlincioni, Luca Cultrera, Chiara Albisani, Lisa Cresti, Andrea Leonardo +3 more
4/13/2023
cs.CV

No official code found. Best implementation: miccunifi/NEFER (30 stars, last commit 2023-12-07).

Abstract

Recently, event cameras have shown large applicability in several computer vision fields especially concerning tasks that require high temporal resolution. In this work, we investigate the usage of such kind of data for emotion recognition by presenting NEFER, a dataset for Neuromorphic Event-based Facial Expression Recognition. NEFER is composed of paired RGB and event videos representing human faces labeled with the respective emotions and also annotated with face bounding boxes and facial landmarks. We detail the data acquisition process as well as providing a baseline method for RGB and event data. The collected data captures subtle micro-expressions, which are hard to spot with RGB data, yet emerge in the event domain. We report a double recognition accuracy for the event-based approach, proving the effectiveness of a neuromorphic approach for analyzing fast and hardly detectable expressions and the emotions they conceal.

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Code Implementations(1)

miccunifi/NEFERCommunity100%

[CVPR 2023] NEFER a Dataset for Neuromorphic Event-based Facial Expression Recognition

301Apr 13, 20232 years ago

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Cite this paper

@article{berlincioni2023neuromorphic,
  title  = {Neuromorphic Event-based Facial Expression Recognition},
  author = {Lorenzo Berlincioni and Luca Cultrera and Chiara Albisani and Lisa Cresti and Andrea Leonardo and Sara Picchioni and Federico Becattini and Alberto Del Bimbo},
  year   = {2023},
  eprint = {2304.06351},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2304.06351v1}
}

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