MUTFER2024: A new dataset for South African emotion recognition

Facial Emotion Recognition (FER) plays a critical role in applications such as human-computer interaction, security, and healthcare. The effectiveness of FER systems largely depends on the quality and diversity of the datasets used for training and evaluation. However, existing FER datasets often lack adequate representation of African populations, leading to racial biases in recognizing emotions across diverse ethnic groups. This issue arises from the predominance of Western-centric datasets used in training FER systems, which results in inaccurate and biased outcomes when applied to African or non-Caucasian faces.

To address this limitation, we introduce MUTFER2024, a novel dataset developed at Mangosuthu University of Technology. MUTFER2024 aims to minimize racial bias in FER systems by providing an extensive collection of facial emotion images from African participants. The dataset comprises 13,032 images collected from 300 individuals, including students and staff members, and is categorized into seven emotion classes: happy, sad, angry, surprised, neutral, disgusted, and fearful.

This paper details the methodology employed in data collection, segmentation, and categorization. Facial emotion images were gathered through structured submission protocols to ensure diversity in expressions. Subsequently, the images were meticulously segmented and categorized into the specified emotion classes. Data were collected under real-world conditions using mobile and computer cameras. The dataset is hosted on GitHub and can be used to train emotion recognition models for underrepresented African populations.