Color histogram equalization and fine-tuning to improve expression recognition of (partially occluded) faces on sign language datasets

Figure 1: Example pictures of our procedure to classify Ekman emotions.The original picture is first normalized in size, rotation, and color profile.Then, we perform classification on the full face, only top part, and only bottom part.On the left: examples from the AffectNet dataset.

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Published
2025-09-16
DOI
https://doi.org/10.1145/3742886.3756731
Primary Topic
Hand Gesture Recognition Systems
Type
article
Field-Weighted Citation Impact
0.00

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article

Color histogram equalization and fine-tuning to improve expression recognition of (partially occluded) faces on sign language datasets

Fabrizio Nunnari, Patrick Gebhard
Hand Gesture Recognition Systems
article

Color histogram equalization and fine-tuning to improve expression recognition of (partially occluded) faces on sign language datasets

Fabrizio Nunnari, Patrick Gebhard
article en

Abstract

Figure 1: Example pictures of our procedure to classify Ekman emotions.The original picture is first normalized in size, rotation, and color profile.Then, we perform classification on the full face, only top part, and only bottom part.On the left: examples from the AffectNet dataset.

German Research Centre for Artificial Intelligence (DE)
Bundesministerium für Bildung und Forschung
Openalex Percentile: Top 73%
Hand Gesture Recognition Systems
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Color histogram equalization and fine-tuning to improve expression recognition of (partially occluded) faces on sign language datasets — Fabrizio Nunnari, Patrick Gebhard · (2025) | TGRS Research Map | TGRS