text: Title: [Question] Face verification does not meet expectations
  Body: I am very thankful for this open source project, it solved my immediate problem.
  
  I am trying to accomplish the task of face comparison with yolov8n-face and Facenet512. But for some samples, the results are not too good. 
  
  I tried to replace the face detection with a paid api and this is my test result:
  
  |Face detection type|x|y|w|h|left eye|right eye|Facenet512 cosine distance  |
  |---|---|---|---|---|---|---|---|
  |yolov8n-face.pt img1|43|167|242|334|(99, 288)|(216, 290)| 0.2299605098248957|
  |yolov8n-face.pt img2|170|165|405|541|(280, 377)|(457, 372)| |
  |yolov8n-face.onnx img1|43|166|241|333|(99, 289)|(216, 291)|0.22794579406637527 |
  |yolov8n-face.onnx img2|168|160|401|544|(278, 380)|(456, 376)| |
  |payment api img1|44|229|245|271|(100, 284)|(220, 283)| 0.3275035163254295|
  |payment api img2|169|307|385|414|(279, 376)|(453, 362)| |
  
  On the left is the yolov8 detection area and on the right is the paid api detection area:
  ![test](https://github.com/serengil/deepface/assets/43714368/7e8fc61b-eeed-4c30-83af-6adf007b8445)
  
  Now I'm confused. Based on the recommended threshold (0.3), the face region obtained from the paid api seems to be better suited to accomplish the face matching task. So should I find a way to shrink the face region or should I find a more suitable threshold based on my actual usage scenario?
  
  Can you provide optimization suggestions?
  
  