SCANet: Real-Time Face Parsing Using Spatial and Channel Attention

This paper presents a real-time face parsing method that is efficient and robust to small facial components. The proposed approach utilizes two separate attention networks, namely the Spatial and Channel Attention Networks (SCANet), to integrate local features with global dependencies and focus on the most critical contextual features. Specifically, the Spatial attention module (SAM) captures the spatial relationships between different facial features, while the Channel attention module (CAM) identifies important features within each channel of the feature map, such as skin texture or eye color. Moreover, an edge detection branch, which helps differentiate edge and non-edge pixels, is added to improve segmentation precision along edges. To address class imbalance issues, which arise from limited data on accessories such as necklaces and earrings, we utilize a weighted cross-entropy loss function that assigns higher weights to rare classes. The proposed method outperforms state-of-the-art methods on the CelebAMask-HQ dataset, especially in small facial classes like necklaces and earrings. Additionally, the model is designed to operate in real-time, making it a promising solution for various face recognition and analysis applications.