In recent years, numerous surveillance camera systems have been installed to enhance security measures and monitor public spaces. This has created a growing demand for effective Person Re-Identification (Person ReID) models capable of processing large volumes of data swiftly and accurately. The Lightweight Multi-Branch Network (LightMBN) model addresses these needs by providing a high-accuracy, efficient solution. In this study, we aim to further enhance the LightMBN model by integrating additional data modalities, including segment, skeleton, and attribute information. These enhancements are designed to offer a more comprehensive representation of individuals, thereby improving identification accuracy. Through meticulous experimentation with various hyperparameters, we evaluate the performance of our enhanced model. The results show a significant improvement in mean Average Precision (mAP) compared to the original LightMBN, achieving state-of-the-art performance with an mAP of 84.5% without re-ranking and 91.5% with re-ranking on the CUHK03 detected (CUHK03-D), and 86.7% and 93.0% respectively on the CUHK03 labeled (CUHK03-L). Our findings suggest that incorporating diverse data types significantly enhances the effectiveness of Person ReID systems, paving the way for more robust and reliable surveillance applications.