## 3 Methods
### Feature Extraction Using DINO  
DINOv2 [44] is an advanced self-supervised learning model that improves the training of robust visual features without supervision. In this study, we selected the DINOv2  
Figure 4: DINOv2 and Multi-Layer Perceptrons. (**a**) DINOv2; (**b**) Layer 1; (**c**) Layer 2; (**d**) Layer 3.  
self-supervised learning framework for feature extraction due to its distinct advantages. First, DINOv2 eliminates the need for complex preprocessing, such as extensive data augmentation or label processing, enabling the direct use of raw images for training, thus simplifying the workflow and reducing dataset preparation time. Second, DINOv2 excels in feature representation by maximizing consistency between homologous images, allowing the model to learn robust representations essential for tasks like sketch feature extraction. Third, its self-supervised approach enables feature extraction from large-scale image datasets without manual annotations, making it ideal for handling vast amounts of unlabeled data. Finally, DINOv2 demonstrates strong robustness in recognizing key elements in images, particularly for precise sketch feature extraction. Overall, its simplicity, robustness, and self-supervised capabilities make DINOv2 an optimal choice for our task, offering efficiency and strong performance without relying on extensive labeled datasets. It trained on diverse datasets providing a broad perspective, including ImageNet-22k, the train split of ImageNet-1k, Google Landmarks, and an assortment of fine-grained datasets comprising 1.2 billion unique images. DINOv2 showcases significant advancements in handling various computer vision tasks without fine-tuning.  
The DINO framework employs a dual-network architecture consisting of a student and a teacher network. The student network learns by attempting to replicate the output of the teacher network, which in turn is an exponential moving average of the student's parameters. The core process involves generating multiple augmented views (\(I_{1},I_{2},\ldots,I_{k}\)) of a given input image (\(I\)), which then these networks process. The resultant feature vectors from the student (\(F_{s}\)) and teacher (\(F_{t}\)) networks are utilized to compute the distillation loss as follows:  
\[\mathcal{L}_{\text{D}}=\sum_{i=1}^{k}\text{CrossEntropy}\bigg{(}F_{s}(I_{i}), \text{Softmax}\bigg{(}\frac{F_{t}(I_{i})}{\tau}\bigg{)}\bigg{)}, \tag{1}\]  
where \(\tau\) represents the temperature scaling parameter. The output functions for DINOv2 can be denoted as \(f_{\text{DINOv2}}(\mathcal{I})\), converting an input image \(\mathcal{I}\) of size \(224\times 224\times 3\) into a \(1\times 384\) feature vector.  
Since the eyeglass frames often showcase intricate curves, sharp edges, and sophisticated geometric shapes, we utilize the DINOv2 model based on the Vision Transformer (ViT) architecture as our encoder \(\mathcal{E}\) to extract features \(\mathcal{Z}\) from the FDS of the eyeglass frame, which allows us to identify a diverse range of geometric features effectively.  
Given an input sketch \(\mathcal{I}\), we employ the DINOv2 model to extract features and project them to a fixed dimension using a linear layer,  
\[\mathcal{Z}=linear(f_{\text{DINOv2}}(\mathcal{I})). \tag{2}\]