{'fraction_non_alphanumeric': 0.010362694300518135, 'fraction_numerical': 0.0025906735751295338, 'mean_word_length': 5.3884297520661155, '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}}
Embedded face and facial expression recognition

A framework for embedded recognition of faces and facial expressions is described. Faces are modeled based on the appearances and positions of facial features. Hidden states are used to represent discrete facial expressions. A face model is constructed for each person in the database using video segments showing different facial expressions. Face recognition and facial expression recognition are carried out using Bayesian classification. In our current implementation, the face is divided into nine facial features grouped in four regions which are detected and tracked automatically in video segments. We report results on face and facial expression recognition using a video database of 18 people and six expressions.