A Lightweight and Multi-Branch Module in Facial Semantic Segmentation Feature Extraction

Face recognition has been one of the most studied researches in computer vision, and facial feature extraction, is one of the cores of face recognition. In this paper, we focus on semantic segmentation methods for facial feature extraction. However, the structure and number of parameters of the model usually affect the accuracy of semantic segmentation tasks in facial feature extraction. High-resolution representation learning can bring better accuracy to semantic segmentation, and large convolutional kernels, which bring a larger effective receptive field and better shape feature extraction to the model, can also improve the performance of the segmentation model. In this paper, we propose a novel lightweight model, HRModel (HRM), for optimizing the accuracy and parameters of semantic segmentation tasks in face recognition. From the perspective of multi-scale fusion, large perceptual fields, and gradient segmentation using high-resolution parallel streaming networks as a framework, we use a multi-scale feature extraction module with a large convolutional kernel to reduce the number of parameters while maintaining accuracy. In the feature extraction block, we use a channel attention mechanism to aggregate feature maps of different depths and different receptive fields. At the same time, we apply depthwise separable convolution and gradient flow splitting to reuse the gradient information and hence reduce the number of parameters and computational effort of the network effectively. We compare our proposed model HRM with other single-model networks on semantic segmentation tasks for face recognition. The experimental results show that HRM significantly reduces the number of parameters and achieves an accuracy of 95.52% in the application of the CelebAMask-HQ dataset.