Augmenting RetinaFace Model with Conditional Generative Adversarial Networks for Hair Segmentation

In this paper, an augmentation of the RetinaFace model for hair segmentation is proposed by incorporating a Conditional Generative Adversarial Network (cGAN). The proposed model is trained to generate high-quality hair segmentation masks by considering various hair textures, colors, and styles. Our approach is based on the idea that hair segmentation can benefit from the use of cGANs, because they can learn to generate realistic hair images and help improve the performance of RetinaFace. Experimental results show that our model outperforms the RetinaFace model on several benchmarks, achieving state-of-the-art performance.