Th Face Quality library uses the face quality network to detect the quality score of a face. If a face is clear and a front face, the score is high. On the contrary, a blurry or side face will get a low score. The scores range from 0 to 1. It also provides face landmark positions. The input is a face that is detected by the face detect network and the output contains a quality score and five landmark key points.
Figure 1. Face Quality Example
The following table lists the face quality models supported by the Vitis AI Library.
| No | Model Name | Framework |
|---|---|---|
| 1 | face-quality | Caffe |
| 2 | face-quality_pt | PyTorch |