zyh-uaiaaaa/Relative-Uncertainty-Learning
Official implementation of our NeurIPS2021 paper: Relative Uncertainty Learning for Facial Expression Recognition
This project helps behavioral researchers and marketers accurately interpret facial expressions, even when expressions are ambiguous or inconsistently labeled. It takes images of faces as input and outputs a more reliable classification of emotions (like happy, sad, angry) by accounting for the inherent uncertainty in facial cues. The primary users are researchers or practitioners working with human emotion data from facial analysis.
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Use this if you need to classify facial expressions with high confidence from image data, especially when dealing with ambiguous expressions or noisy labeling in your datasets.
Not ideal if your primary goal is general object recognition or if you require real-time, low-latency facial expression analysis for streaming video.
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55
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6
Language
Python
License
—
Last pushed
Oct 21, 2022
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