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Hi,

Lets take some steps back . You wrote " Up to there it works, but processing the face at every frame, what reduces FPS from 20 to 2 in some seconds" ...

So you face detection will not be in real time. I would work on the detection performance of the face. For example you could run a cnn model on gpu to detect the face. With that you should be able to detect in real time.

The other solution would be to use the tracking api. Basically you detect the object once in a frame and detect it in subsequent frames via the tracking api. But once again - this could be expensive too and maybe wont solve your problem(false positive detection, etc..).

So i would recommend you to work on making the face detection perform in real time.

Hi,

Lets take some steps back . You wrote " Up to there it works, but processing the face at every frame, what reduces FPS from 20 to 2 in some seconds" ...

So you face detection will not be in real time. I would work on the detection performance of the face. For example you could run a cnn model on gpu to detect the face. With that you should be able to detect in real time. time.

OpenCV let you detect face via a cnn model(performance is around 38 ms on cpu). Check this: https://www.pyimagesearch.com/2018/02/26/face-detection-with-opencv-and-deep-learning/

The other solution would be to use the tracking api. Basically you detect the object once in a frame and detect it in subsequent frames via the tracking api. But once again - this could be expensive expensive(i dont have times for the tracking api yet) too and maybe wont solve your problem(false positive detection, etc..).

So i would recommend you to work on making the face detection perform in real time.