Technical test David Campoy#50
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| 1. How can we improve face detection by adding **reidentification** to anonymize only specific individuals while keeping computational cost low? | ||
| In general, to reduce the computational cost of any ML model one can reduce the model size (e.g. a model with less parameters/layers, use quantization, etc.). However, this could reduce the model accuracy if not done properly. | ||
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| To maintain precision while reducing computational cost, we can run our face detection model at the very first frame, extract the detected face embeddings, compare them againts a "face gallery", and if the similarity is above a threshold, then anonymize. In the following frames, we can implement a face tracker to follow the faces in the video (e.g. by using a Kalman filter) and re-run the face detection + reidentification model every 5-10-15 frames (depending on how fast people move, or triggered by a tracker confidence drop). This way we can recenter (if needed) the centroid of the already detected faces and detect new ones. This strategy creates a tradeoff between computational cost and face detection speed, but it can be resolved at an optimal point where computational cost is reduced by a factor 10x with equivalent performance. |
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