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 Face recognition is a key computer vision technology that is widely used in identity verification, security monitoring, and social media. The model implementation is divided into two parts, one is the image recognition and cutting of yolo method, the other is the code part and the model implementation part. The project combines feature extraction methods such as PCA, NMF, and CNN to analyze existing schemes and propose an optimization scheme that combines traditional methods and deep learning to improve the performance and flexibility of face recognition systems 
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Week 1

   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...
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Week 2

   Week 2: （2025/2/9） This week, through division of labor and collaboration, we have completed the preliminary coding of the YOLO v11 model, and the model can more accurately identify and segment objects in images. Aim for week 2: Debugging, configuration and implementation of YOLO face detection model; Develop and optimize the face recognition model, and gradually improve the recognition performance of the model; Introduce automated scripts Implementation of tasks: Siqi Jia, Jinrun Tan: 1. Introduce automated scripts to handle the time-consuming processing of documents in multiple language versions to ensure consistency and accuracy of the documents. 2. Test model export, confirm callback information, and export functionality is normal. 3. Detect image segmentation effects and use segmentation models to train COCO segmentation datasets FulinYang : 1. The method of early stopping and learning rate self modification was used to reduce the overfitting of the YOLO V11 model. 2. ...
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Week 3

  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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Week 4

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  Through the first three weeks we have mastered how to train YOLO and SVM. Here's the code flowchart and the final demo video. 
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