How to use extracted features about mobileface HOT 8 CLOSED

Maybe the v3 is for experiment.

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Maybe the v3 is for experiment.

but the v2 has a similar result

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@zhxj9823
You can find the method to calculate the similarity of features in ConfusionMatrix_similarity_visualization.py#L55

BTW, I'm tied up at the moment. I will update this repo. when I am free.

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@zhxj9823
You can find the method to calculate the similarity of features in ConfusionMatrix_similarity_visualization.py#L55

BTW, I'm tied up at the moment. I will update this repo. when I am free.

I have tried the same method but the accuracy is still low. I wonder if there are some requirements for the images. For example, do images need to be in grayscale or RGB-mode? Do images contains only frontal faces? I tried on LFW datasets, and it performs well, but on my own dataset where there are a lot of side faces, the distances between images of the same person are too big to find a proper threshold.

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@zhxj9823
All these cases of dataset of train, face position and quality, input size and color, keypoint and align will affect the recognition results. More detial you can reference insightface.

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@becauseofAI
Actually, I used insightface first. The accuracy is pretty high on my own testing dataset, but the inference time is relatively long, so I turn to your model, but the accuracy on the same dataset is too low for me.

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So it's a trade-off of the speed and accuracy.
In fact, the v1 is is suitable the scene for certificate photo, v2 got reasonable accuracy on datasets of lfw, agedb_30 and cfp_fp, and v3 was an extreme try.
I will train and test the detection, key points and recognition models based on a same cross-scenario data when I am free, then update the process of all the code. I will not reply before that.

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@becauseofAI Thanks for your clarification. The accuracy of mobileface I tested on my dataset is less than 60%, but it can be up to 99% when I use insightface. The accuracy seems somewhat unreasonable here.

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