{'fraction_non_alphanumeric': 0.016546018614270942, 'fraction_numerical': 0.001034126163391934, 'mean_word_length': 5.245161290322581, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A self-calibrating chrominance model applied to skin color detection

In case of the absence of a calibration procedure, or when there exists a color difference between direct and ambient light, standard chrominance models are not completely brightness invariant. Therefore, they cannot provide the best space for robust color modeling. Instead of using a fixed chrominance model, our method estimates the actual dependency between color appearance and brightness. This is done by fitting a linear function to a small set of color samples. In the resulting self-calibrated chromatic space, orthogonal to this line, the color distribution is modeled as a 2D Gaussian distribution. The method is applied to skin detection, where the face provides the initialization samples to detect the skin of hands and arms. A comparison with fixed chrominance models shows an overall improvement and also an increased reliability of detection performance in different environments.