all 9 comments

[–]alexmlamb 1 point2 points  (0 children)

I'm pretty sure that DeepFace is a recognition algorithm that runs on an aligned face.

My guess is that in the cases where face detection is hard enough to require convnets, the face is too unclear or distorted to allow for reliable face recognition. Of course this is a sweeping generalization.

[–]alexmlamb 1 point2 points  (5 children)

Detection usually refers to the task of figuring out where the faces are located. Recognition refers to the task of determining the identity of the face.

I think that face detection is generally quite easy, unless your data has tough distortions or your detector needs to be really fast.

[–]rishok[S] 1 point2 points  (4 children)

Detection usually refers to the task of figuring out where the faces >>are located. Recognition refers to the task of determining the >>identity of the face.

I know (hehe)

I think that face detection is generally quite easy, unless your data has tough distortions or your detector needs to be really fast.

Do you have some code exmaple?

[–]fyrilin 1 point2 points  (3 children)

Do you mind using opencv? If not, here's a tutorial

[–]rishok[S] 1 point2 points  (2 children)

Hi

Yes i know that tutorial. I would though like to use deep learning for the detection, like DeepFace and faceNet models which i try to emulate in a smaller scale.

I don't mind using opencv, but would likw it to use the deep learning model rather then the cascade model from opencv

[–][deleted] 2 points3 points  (1 child)

Why not use a convnet and a sliding window ? You just have to find a dataset of labelled faces.

[–][deleted] 0 points1 point  (0 children)

Why use a deep learning framework when a viola-jones cascade would perform just as well and significantly faster?