Deep Learning Based Multi Pose Human Face Matching System

作者：Muhammad Sohail, Ijaz Ali Shoukat, Abd Ullah Khan, Haram Fatima, Mohsin Raza Jafri, Muhammad Azfar Yaqub, Antonio Liotta · 发表于：IEEE Access · 年份：2024 · DOI：10.1109/access.2024.3366451 · 被引用次数：22 · 研究领域：Face recognition and analysis、Face and Expression Recognition、Video Surveillance and Tracking Methods

Current techniques for multi-pose human face matching yield suboptimal outcomes because of the intricate nature of pose equalization and face rotation. Deep learning models, such as YOLO-V5, etc., that have been proposed to tackle these complexities, suffer from slow frame matching speeds and therefore exhibit low face recognition accuracy. Concerning this, certain literature investigated multi-pose human face detection systems; however, those studies are of elementary level and do not adequately analyze the utility of those systems. To fill this research gap, we propose a real-time face matching algorithm based on YOLO-V5. Our algorithm utilizes multi-pose human patterns and considers various face orientations, including organizational faces and left, right, top, and bottom alignments, to recognize multiple aspects of people. Using face poses, the algorithm identifies face positions in a dataset of images obtained from mixed pattern live streams, and compares faces with a specific piece of the face that has a relatively similar spectrum for matching with a given dataset. Once a match is found, the algorithm displays the face on Google Colab, collected during the learning phase with the Robo-flow key, and tracks it using the YOLO-V5 face monitor. Alignment variations are broken up into different positions, where each type of face is uniquely learned to have its own study demonstrated. This method offers several benefits for identifying and monitoring humans using their labeli...