{'fraction_non_alphanumeric': 0.020151133501259445, 'fraction_numerical': 0.0069269521410579345, 'mean_word_length': 5.539094650205762, '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}}
Context‐based robust face detection algorithm for surveillance cameras

This paper describes a context‐based robust face detection algorithm for surveillance cameras. Different from familiar faces captured by digital cameras, faces captured by surveillance cameras are smaller and darker with motion blurs and distortions. Furthermore, captured from top‐mounted cameras, facial images are downward and partially hidden. Just using a single‐face detector to detect such degraded faces is very difficult. To solve the problem, we utilize contextual information about faces of walking people. We employ a probabilistic face detection framework combining a face detector with local and global contextual information. We use a boosted fast face detector as an initial selector to pick up a small number of possible face regions in a very short time. After the fast selection of candidate face patches, as local contextual information we calculate a conditional probability in the surrounding regions using a histogram of oriented gradient (HOG) feature‐based outline detector, and as global contextual information we calculate possible face patches from viewpoint information using vanishing point detection. Combining a fast boosted face detector with these contextual information, while keeping computational efficiency of the original boosted face detector, we achieved a high face detection rate of 93.7% with about 1000 times lower false‐positive rate than when using a single original face detector. © 2012 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.