Deep facial representation learning has achieved remarkable performance in computer vision applications; however, high overall accuracy does not necessarily ensure equitable performance across demographic groups. This study presents a unified framework for evaluating the quality and demographic fairness of pretrained d...
Andisani Nemavhola, Serestina Viriri, C. Chibaya· Scientific Reports· 0 citations
The findings demonstrate that additive angular margin supervision does not universally outperform conventional Softmax optimization and highlight the importance of multi-dataset benchmarking, fold-level evaluation, and supervision sensitivity analysis for robust and reproducible face recognition benchmarking.
Andisani Nemavhola, C. Chibaya, Serestina Viriri· Frontiers in Artificial Inte...· 1 citation
A statistically grounded evaluation of demographic bias in face attribute classification across three representative architectures, ResNet50, MobileNetV3, and a vision transformer, using the FairFace and UTKFace datasets shows that race-based disparities are substantially larger than gender-based disparities across bot...
Andisani Nemavhola, Serestina Viriri, C. Chibaya· Frontiers in Artificial Inte...· 0 citations
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