ROI-YOLOv8-Based Far-Distance Face-Recognition
Felix Gunawan, Chih‐Lyang Hwang, Zih‐En Cheng · 2023
This paper presents a model for far-distance face recognition using ROI-YOLOv8. We achieve this by training YOLOv8 for 3 target faces on our custom datasets: (i) The first dataset is the original dataset with training and validation images of 2412 and 229, respectively. (ii) The second dataset augments the more pixelated images from the 1stone with training and validation images of 4824 and 458, respectively. (iii) The third dataset considers various exposure, noise, and blur from the 2ndone with training and validation images of 14,272 and 459, respectively. To enhance the far-distance recognition, a two-stage recognition is considered. At first, a pre-trained YOLOv8 model for human detection is achieved. A Region-of-Interest (ROI) including detected humans is segmented as the size of 640 × 640 pixels for the input of another YOLOv8, i.e., ROI-YOLOv8-FR. A computer with Intel i5-12400F, 16GB RAM, and NVIDIA RTX 3080Ti with 12GB VRAM is used as computing platform. The trained times for these 3 datasets are 50, 95, and 219 minutes, respectively. Their mAP50s are 99.5% and mAP50-95s are slightly different as 88.112%, 87.962%, and 88.103% respectively. More important, the 3rdtrained model can successfully recognize a face at 30 and 35m with the confidences of 65.1% and 75.6%.