Hand Gesture Recognition (HGR) achieved significant progress through diverse fields due to recent advancements in machine learning and sensor technologies. While Leap Motion Controller sensors offer convenient hand tracking and multi-modal data (skeletal and depth), the heterogeneous nature of these data modalities poses several challenges for HGR systems. In order to exploit the complementary information offered by skeleton and depth data, fusion algorithms are widely used. This paper proposes a novel Deep CNN-BiGRU model incorporating both intermediate and late fusion strategies. For each modality, we use a separate model for feature extraction step. Then, we apply fusion techniques for the decision step. Our proposed model demonstrates superior performance compared with models employed separately on skeletal or depth data, highlighting its effectiveness in exploiting the combined information for robust and accurate HGR.

Person Re-Identification is a critical component in modern video surveillance systems for locating individuals across cameras from various viewpoints. However, one of the significant challenges in person ReID arises when facial information is unavailable. To address this issue, we propose GF2PReID, a novel framework that leverages state-of-the-art deep learning-based ReID approaches to generate prior knowledge of the face region and provide detailed information about human body images. Our approach utilizes a deep residual network model trained with transfer learning to extract discriminative features from images with low discrimination, including low illumination and occlusion, collected from diverse datasets. Experimental results, on 2 challenging datasets: Market-1501 and CUHK03, demonstrate that our GF2PReID framework improves the datasets and significantly improves the performance of the Resnet-50 model, reaching the highest performance.