Balancing the Scales: Enhancing Fairness in Facial Expression Recognition with Latent Alignment

• URL: http://arxiv.org/abs/2410.19444v1
• Date: Fri, 25 Oct 2024 10:03:10 GMT
• Title: Balancing the Scales: Enhancing Fairness in Facial Expression Recognition with Latent Alignment
• Authors: Syed Sameen Ahmad Rizvi, Aryan Seth, Pratik Narang,
• Abstract summary: This workleverages representation learning based on latent spaces to mitigate bias in facial expression recognition systems. It also enhances a deep learning model's fairness and overall accuracy.
• Score: 5.784550537553534
• License:
• Abstract: Automatically recognizing emotional intent using facial expression has been a thoroughly investigated topic in the realm of computer vision. Facial Expression Recognition (FER), being a supervised learning task, relies heavily on substantially large data exemplifying various socio-cultural demographic attributes. Over the past decade, several real-world in-the-wild FER datasets that have been proposed were collected through crowd-sourcing or web-scraping. However, most of these practically used datasets employ a manual annotation methodology for labeling emotional intent, which inherently propagates individual demographic biases. Moreover, these datasets also lack an equitable representation of various socio-cultural demographic groups, thereby inducing a class imbalance. Bias analysis and its mitigation have been investigated across multiple domains and problem settings, however, in the FER domain, this is a relatively lesser explored area. This work leverages representation learning based on latent spaces to mitigate bias in facial expression recognition systems, thereby enhancing a deep learning model's fairness and overall accuracy.

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