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The sample notebook infer.ipynb implies that mtcnn() returns the aligned images (x_aligned), but it doesn't! I've tested it on some non-aligned faces, and the faces weren't rotated (rotation is described here: https://sefiks.com/2020/09/09/deep-face-detection-with-mtcnn-in-python).
What's the sense in the landmarks if you don't align the image? And why mtcnn() doesn't return the landmarks if you want to leave the rotation to the user?
The text was updated successfully, but these errors were encountered:
I'm guessing this is because 1 method for cropping/alignment shown in the paper doesn't do alignment at all, but simply crops the area where a face is detected and feeds that to resnet portion of the model:
Our model is evaluated in two modes:
1. Fixed center crop of the LFW provided thumbnail.
2. A proprietary face detector (similar to Picasa [3]) is run on the provided LFW thumbnails. If it fails to align the face (this happens for two images), the LFW alignment is used.
Figure 6 gives an overview of all failure cases. It shows false accepts on the top as well as false rejects at the bottom. We achieve a classification accuracy of 98.87%±0.15 when using the fixed center crop described in (1) and the record breaking 99.63%±0.09 standard error of the mean when using the extra face alignment (2)
The sample notebook infer.ipynb implies that mtcnn() returns the aligned images (x_aligned), but it doesn't! I've tested it on some non-aligned faces, and the faces weren't rotated (rotation is described here: https://sefiks.com/2020/09/09/deep-face-detection-with-mtcnn-in-python).
What's the sense in the landmarks if you don't align the image? And why mtcnn() doesn't return the landmarks if you want to leave the rotation to the user?
The text was updated successfully, but these errors were encountered: