To fine-tune the Face detection tool, do as follows:

1. Configure face rotation angle analysis:
   
   1. If it is necessary to determine the face rotation angle, then set Yes for the Analyze face rotation angle parameter (1).
      
   2. In the Face rotation pitch (°) field (3), set the allowable face tilt up/down angle in degrees. The value should be in the range [0; 90].
   3. In the Face rotation roll (°) field (4), set the allowable face tilt right/left angle in degrees. The value should be in the range [0; 90].
   4. In the Face rotation yaw from (°) field (5), set the minimum allowable angle of face rotation to the right or left. The value should be in the range [-90; 90].

   5. In the Face rotation yaw to (°) field (6), set the maximum allowable angle of face rotation to the right or left. The value should be in the range [-90; 90].

2. Select a face detection algorithm (2):
   
   1. ALG1 − high speed, low accuracy.
   2. ALG2 − average speed, average accuracy.
   3. ALG3 − low speed, high accuracy.
3. Set up false mask detections filtering:
   1. To apply filters, set Yes for the False mask detections filtering parameter (7).
   2. Set the minimal percentage of probability which makes the additional algorithm identify a track as a masked face in the Minimum filtering threshold for face mask detection field (13). If the algorithm takes a decision that the track relates to a masked face with a probability value lower than the specified threshold, the track will be ignored. Set the value by trial-and-error, values over 30 are recommended.
4. Set the minimum threshold value for mask detection (9). Set a value by trial-and-error, values over 70 are recommended.
5. Set the minimum quality of a face image for recognition with a mask (10, see Configuring masks detection). Set the value by trial-and-error, values over 30 are recommended.
6. Set the minimum quality of a face image for recognition without a mask (11). Set the value by trial-and-error, values over 50 are recommended.
7. If it is necessary to filter out false positives, set the minimum percentage of probability which makes the algorithm identify a track as a human face in the Minimum filtering threshold field (12, see Configuring Face detection). If the algorithm takes a decision that the track relates to a face with a probability value lower than the specified threshold, the track will be ignored. Set the value by trial-and-error, values over 50 are recommended.
8. If it is necessary for the detection tool to use a color frame for processing, then set Yes for the Process color frames parameter (15). By default, a black and white frame is processed.
9. Configure the repeated face recognition ignoring:
   1. If it is necessary to ignore repeated recognition of the same face, then set Yes for the Ignore repeated recognitions parameter (8).
   2. In the Repeated recognitions similarity threshold field (16), set the similarity threshold of a face with the previous recognized ones in percentage from 0 to 100. If the similarity threshold is below the specified value, then the face will be recognized as a new one.
   3. In the Period of ignoring repeated recognitions field (14), set the period in minutes during which new recognized faces will be compared with the previous ones to identify similarities. The value should be in the range [0; 30].
10. Click the Apply button.

Fine-tuning the Face detection tool is now complete.