{'fraction_non_alphanumeric': 0.018404907975460124, 'fraction_numerical': 0.0, 'mean_word_length': 5.663265306122449, '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 unified stochastic model for detecting and tracking faces

We propose merging face detection and face tracking into a single probabilistic framework. The motivation stems from a broader project in algorithmic modeling, centered on the design and analysis of the online computational process in visual recognition. Detection is represented as a tree-structured graphical network in which likelihoods are assigned to each history or "trace" of processing, thereby introducing a new probabilistic component into coarse-to-fine search strategies. When embedded within a temporal Markov framework, the resulting tracking system yields encouraging results.