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


Possible reasons:

1. Low detection accuracy: Even with the most advanced model YOLO11x, face detection is still unreliable.


2. Possible dataset mismatch: The LFW dataset may not match the training data of YOLOv11, resulting in poor performance.

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