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 benefits of choosing YOLO for face detection; 3, the method of training YOLOv5 for face detection; 4, confirmed the python code needed for inference (face detection). 5. Implementation method of YOLO (divided into data set selection, data annotation format and training process)

Siqi Jia, Jinrun Tan: 1, confirming the best YOLO version (YOLOv8) for completing the model. 2, explains why YOLOv8 is the best version to complete the project. 3. Configure the steps for setting YOLOv8. 4. Complete the installation steps and initial operation of YOLOv8.

Peng Wang: 1, developed a facial recognition optimization system combining traditional feature extraction methods and deep learning models. 2, use the code from the scikit-learn website example for a preliminary test. 3. NMF technology was introduced to optimize the model in combination with PCA and SVM. 4. Performance evaluation after model optimization

Ziyu Huang, Yicheng Sheng: 1. Use CNN (Convolutional neural network) to replace NMF for face recognition and complete the secondary optimization of the model. 2. Solve the misjudgment problem of Donald Rumsfeld and Gerhard Schroeder, and improve the recognition rate of Hugo Chavez categories; 3. Integrate data enhancement technology and introduce FaceNet for feature extraction

Issues and solutions:

Issue 1：

Small face detection:

Solution：

Appropriately increase the input resolution or multi-scale training, adding more small face samples.

 

Issue 2：

Too many false checks:

Solution：

Adjust the confidence threshold, or optimize the data quality.

 

Issue 3：

Lack of speed:

Solution：

Use lightweight models (like YOLOv5s or n versions), or speed up inference on Gpus/TensorRT/OpenVINO.)