{'fraction_non_alphanumeric': 0.02125506072874494, 'fraction_numerical': 0.0, 'mean_word_length': 5.506578947368421, '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 based on Facial Feature and Multi Library Wavelet Neural Network

In this paper, we propose a wavelet neural network-based system for automatically classifying facial expressions. This system is based on Multi Library Wavelet Neural Network (MLWNN) for emotions classification. Like other methods, our approach relies on facial deformation features. Eyes, mouth and eyebrows are identified as the critical features and their feature points are extracted to recognize the emotion. After feature extraction is performed a Multi Library Wavelet Neural Network approach is used to recognize the emotions contained within the face. This approach differs from existing work in that we define two classes of expressions: active emotions (smile, surprise and fear) and passive emotions (anger, disgust and sadness). In order to demonstrate the efficiency of the proposed system for the facial expression recognition, its performances are compared with other systems.