The anonymization quality is ensured even in the case of low-resolution input via fast image processing. We use detectors based on sequential classifications that allow for extremely fast data evaluation. Our sequential detectors are constructed using an automated learning method based on the WaldBoost algorithm. Based on a wide series of images (millions of positive examples, billions of negative examples) this algorithm finds the optimal decision rule, which maximizes both the detection success and the speed of evaluation. Thanks to this unique process, our detectors are extremely fast and precise.
