Boosting fairness for 3D face reconstruction
Zeyu Cui, Jun Yu · 2024
With the increasing significance of 3D face reconstruction technology in various domains, including the metaverse, immersive communication, and medical cosmetology, the precise recovery of geometric shapes from 2D images, regardless of age, gender, or ethnicity, is essential. Recent attention to fairness concerns in 3D face reconstruction has primarily focused on skin color issues, such as albedo estimation, with little consideration for racial bias in facial geometric reconstruction. To address this gap, we first surveyed the most recent 3D face reconstruction methods and commonly used 3D face datasets, confirming the existence of racial bias in the accuracy of 3D face reconstruction. We then developed a fair multilevel 3D face reconstruction system by using data resampling and an asymmetric arc loss, which combines arc face loss and circle loss. Our experimental results show that the system achieves more accurate results on the REALY benchmark.