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Added informal comparison to 3DDFA_V2.

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Emiliana 2020-09-05 12:39:01 +02:00
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@ -103,6 +103,10 @@ Pytorch weights for use with `model.py` can be found [here](https://mega.nz/file
More samples: [Results3.png](https://raw.githubusercontent.com/emilianavt/OpenSeeFace/master/Images/Results3.png), [Results4.png](https://raw.githubusercontent.com/emilianavt/OpenSeeFace/master/Images/Results4.png) More samples: [Results3.png](https://raw.githubusercontent.com/emilianavt/OpenSeeFace/master/Images/Results3.png), [Results4.png](https://raw.githubusercontent.com/emilianavt/OpenSeeFace/master/Images/Results4.png)
Here's an informal [comparison](https://raw.githubusercontent.com/emilianavt/OpenSeeFace/master/Images/3DDFA_V2.png) with [3DDFA_V2](https://github.com/cleardusk/3DDFA_V2) on five arbitrary, non-cherry picked examples from my test data:
![3DDFA_V2_800.png](https://raw.githubusercontent.com/emilianavt/OpenSeeFace/master/Images/3DDFA_V2_800.png)
## Face detection ## Face detection
The landmark model is quite robust with respect to the size and orientation of the faces, so the custom face detection model gets away with rougher bounding boxes than other approaches. It has a favorable speed to accuracy ratio for the purposes of this project. The landmark model is quite robust with respect to the size and orientation of the faces, so the custom face detection model gets away with rougher bounding boxes than other approaches. It has a favorable speed to accuracy ratio for the purposes of this project.