Research on Facial Feature Extraction Method Based on Semantic Segmentation

Abstract

This paper obtains the contour map related to the facial features of the face by extracting important information such as facial features of the face, so as to complete the task of extracting the facial features of the face. With the rapid development of computer software and hardware technologies, facial feature extraction technology has become a research hotspot in computer image processing application scenarios such as face recognition and short video sketch filters. However, the current facial feature extraction technology based on deep learning still has problems such as insufficient contours of the extracted facial features and excessive noise. Therefore, aiming at the shortcomings of the original facial feature extraction methods, this paper proposes a facial feature extraction method based on semantic segmentation. The content of the Larticle is mainly expanded from the following main aspects: First, the development status of semantic segmentation is introduced. Then, an improvement to the semantic segmentation network model is proposed. Finally, the average intersection ratio is used as the evaluation index of the segmentation accuracy of the network model. By comparing the feature maps obtained by different methods, it is found that the improved semantic segmentation network model in this paper can realize the complete extraction of facial features.