Accurate segmentation of wrinkles from facial images forms the basis of wrinkle analysis and acts as an important process in skin aging evaluation. However, Wrinkles have high intraclass variability while shallow wrinkles more closely resemble the texture of the skin. Accurate segmentation of both deep wrinkles and shallow fine lines is therefore still a significant challenge. In this paper, we propose a deep learning-based automatic wrinkle segmentation model named Striped WriNet. Its central module is Striped Attention Module (SAM), which consists of Multi-Scale Striped Attention (MSA) and Global Striped Attention (GSA). The ability of the model to extract contextual information about deep folds and shallow fine lines is improved by involving the geometry of the folds to design multi-scale structures and attention mechanisms. In this paper, we conducted experiments on both the public and private datasets, where the images are finely labeled. For Accuracy (Acc), Dice Score (Dice), and Jaccard Similarity Index (JSI), the proposed method is superior to the current state-of-the-art methods. Ablation experiments and coupling tests are performed to demonstrate the effectiveness of each module and the decoupling of SAM. Finally, this paper innovatively proposes the wrinkle index as a computational method for wrinkle evaluation. The experiments were performed on 2000 wrinkle pictures, and the Pearson coefficient of wrinkle index and the expert clinical score was 0.9399, which proving the robustness of the proposed method in assessing wrinkles under different individuals. The code is available from https://github.com/mingyu6yang/striped-wrinet.