Summary of Balancing the Scales: Enhancing Fairness in Facial Expression Recognition with Latent Alignment, by Syed Sameen Ahmad Rizvi et al.
Balancing the Scales: Enhancing Fairness in Facial Expression Recognition with Latent Alignment
by Syed Sameen Ahmad Rizvi, Aryan Seth, Pratik Narang
First submitted to arxiv on: 25 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses the issue of emotional intent recognition using facial expressions in computer vision, specifically focusing on Facial Expression Recognition (FER). FER relies heavily on large datasets with diverse socio-cultural attributes. However, most real-world datasets are manually annotated, which can introduce demographic biases and class imbalance issues. This work proposes a representation learning-based approach to mitigate bias in facial expression recognition systems, enhancing deep learning model fairness and accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps computers better understand people’s emotions based on their faces. Right now, most computer programs that do this use data that might be biased because it was labeled by humans with different backgrounds and experiences. This can make the program worse at recognizing certain emotions or having trouble understanding people from diverse groups. The goal of this study is to find a way to make these programs more fair and accurate by using special techniques that reduce bias in the data. |
Keywords
» Artificial intelligence » Deep learning » Representation learning
