## 1 Introduction
This is because nearly all pedestrian images in open scenes contain a substantial amount of background, which affects the weight distribution of the network during training and consequently impacts person re-identification (Re-ID) performance. Attention networks can guide the network to focus on salient regions in the image, partially mitigating the impact of background interference. However, attention networks can only focus on specific areas and cannot precisely distinguish pedestrian boundaries, thus failing to eliminate background interference. Especially for complex backgrounds or images where the background occupies a significant portion of the frame, attention networks still fall short of ideal performance. Therefore, accurately identifying the pedestrian foreground in the image and performing feature extraction operations within the foreground region represents a potential method to reduce background interference and enhance person re-identification performance [12]. Furthermore, deep learning methods have a strong demand for data. However, in practical scenarios of pedestrian re-identification, obtaining a large amount of well-annotated cross-camera pedestrian data is challenging due to privacy protection and high annotation costs. Given the current limited dataset size, further mining of useful information hidden in images not only enhances feature representation capabilities and model accuracy but also addresses the issue of insufficient data. Pedestrian edges typically represent abrupt changes in brightness or color in images, revealing object contours, textures, shapes, and  
Figure 1: Challenges in person re-identification. (**a**) illumination change, (**b**) resolution change, (**c**) pose change, and (**d**) occlusion. structures with rich detail features. Although edge features have been widely applied in areas such as object detection, image segmentation, and image enhancement, they are rarely considered in pedestrian re-identification. Introducing edge features into pedestrian re-identification tasks and studying their impact on performance is meaningful. Additionally, the relationships between different parts of pedestrian images constitute another typical hidden feature. These relationships reflect internal dependencies among different semantic components of pedestrians, including rich spatial and connectivity features. Combining these internal relational features with other high-level features (such as local textures and global colors) can provide a more comprehensive description of pedestrian appearance, thereby improving re-identification accuracy and robustness. Motivated by the considerations mentioned above, we propose a novel person Re-ID network that leverages edge-enhanced feature extraction and part-level relationships. Our method integrates global features, edge features, and part-level relationships to achieve precise person matching. In the preprocessing stage, we utilize a fine-tuned fully convolutional network (FCN) to extract binary pedestrian masks from pedestrian images. Subsequently, we perform two key feature extraction processes within the foreground region: (1) Edge-Enhanced Global Feature Extraction: We employ an effective edge detection algorithm to generate pedestrian edge images. These edge images are combined with the original input images. Two deep learning network branches are used to extract pedestrian edge features and global features, respectively. Mutual information exchange between these features enhances the extraction of global features. (2) Inter-part Feature Extraction: We extract the foreground part of the features from (1) and divide it into non-overlapping sub-features along the horizontal direction based on semantics. Descriptors are constructed to reveal the relative relationships between different parts of pedestrians. Finally, we use a well-designed feature fusion layer to deeply integrate the aforementioned diverse features.