Brief introducton:

This model can detect face, eye, iris, ear, nose, mouth and even glasses.
It is based on a YoloV6 nano, which provides high speed (170+ FPS).

 

Number of classes: 7 (face, eye, iris, ear, nose, mouth, glasses)

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System requirements:

Inference time using CPU: 15 ms (on HP Laptop 15-DA0042NH (Processor: Intel(R) Core(TM) i7-8550U CPU))
- Up to 5 ms on GPU (GeForce 1050 TI)

 

Model description:

I made a yolov6 nano object detection model.

This model is basically a face detector, but besides the face it can detect sunglasses, eyes, mouth, nose and even iris and ears. 
I have called this detector an extended face detector, as the detection is extended with the classes mentioned above.

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The advantages of this solution are:

• Very fast, on Nvidia 1050 the inference time is around 5 ms. 

• The inference time is quite good even on CPU, using opencv on an Intel(R) Core(TM) i7-8550U CPU the inference time is 15 ms. 

• It's small and fast enough to run at a reasonable speed on mobile devices. 

• It immediately returns regions of the face, so for simpler usecase it is not necessary to run a separate landmark detector on the face. 

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Some problem that can be solved with the help of this model:

• It is capable of detecting multiple parts of the head, so simple head gaze tracking can be achieved using solvepnp. 

• It can also detect the iris, so simple eye gaze tracking can be built on top of it.

• It gives back a bounding rectangle to the eye, which can be used to implement blink detection. 

• It gives back a bounding rectangle to the mouth, which can be used to implement yawn detection.  

• Since it also detects ears, it is detectable if the ear is covered by something. (This could be a valid usecase for driver monitoring related development.)