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 score range from 0 to 1. It also provide face landmark positions. The input is a face which is detected by face detect network and the output contains 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 |