Week 1: (2025/2/2) This week, we held a panel discussion on the project and confirmed the subject model of "face recognition optimization scheme". After each member understood the scope of knowledge, relevant technology and tool resources needed to carry out the project, we identified and collected all the components needed for the project. And assign the tasks and responsibilities of each member of the project team to the project. Aim for week 1: Debugging, selection, configuration and implementation of YOLO face detection model; Develop and optimize the face recognition model, and gradually improve the recognition performance of the model Implementation of tasks: We divided the team of six into two parts. One part is responsible for debugging, selecting, getting familiar with and configuring YOLO, and the other part is responsible for optimizing the face recognition model through various tools and code. FulinYang : 1, confirmed what is YOLO face detection; 2, the benefit...
Week 3: (2025/2/16) This week, based on the test1.py obtained in the previous two weeks, we attempted to further improve the YOLO and SVM models to enhance the accuracy of facial recognition Aim for week 3: Based on test1.py, the original face recognition model is enhanced by integrating FaceNet and SVM Implementation of tasks: 1. Integrate FaceNet and SVM to enhance the original face recognition model. 2 Download and configure YOLOv111 runtime environment 3 from Ultralytics' official GitHub 200 images were selected from a dataset of 1288 facial images, and five different YOLOv11 models were used to evaluate the facial detection capability of YOLOv11 The result is not satisfactory, YOLOv11 is difficult to accurately detect faces Problems and possible causes: Question 1: YOLOv11 struggled to accurately detect faces, regardless of whether using lighter or more accurate models. The detection confidence is usually too low, and many images cannot record any detected faces at all Possi...
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