{'fraction_non_alphanumeric': 0.015384615384615385, 'fraction_numerical': 0.0, 'mean_word_length': 5.691588785046729, '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}}
Facial Expression Recognition with Multi-channel Deconvolution

Facial expression recognition is an important task in human computer interaction systems to include emotion processing.  In this work we present a Multi-Channel Deconvolution method for post processing of face expression data derived from video sequences. Photogrammetric techniques are applied to deter¬mine real world geometric measures and to build the feature vector. SVM classification is used to classify a limited number of emotions from the feature vector. A Multi-Channel Deconvolution removes ambiguities at the transitions between different classified emotions. This way, typical temporal behavior of facial expression change is considered.