{'fraction_non_alphanumeric': 0.017844396859386154, 'fraction_numerical': 0.0014275517487508922, 'mean_word_length': 5.5514018691588785, '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}}
Skybiometry and AffectNet on Facial Emotion Recognition Using Supervised Machine Learning Algorithms

Nowadays, supervised machine learning aims to mimic human sanity such as recognition of facial emotion, interaction abilities and gaining insights into the environment. This machine learning is being utilized in different forms ranging from the exposure of human increase on the way to the patterns of personal interactions. Facial emotion recognition fundamentally identifies emotion which shapes how humans' self-control and reaction based on situations as well as the environment to which they belong. With these, there are great numbers of researches into developing supervised machine learning to recognize human facial emotions. In recognition of facial emotion, Skybiometry and AffactNet have been employed. Skybiometry is considered to be a state of the art in recognizing and detecting facial expressions. It allows developers and marketers to do more with the use of cloud biometrics api [1]. On the other hand, Mollahosseini prepared, collected and even annotated new database of facial emotions approximately from the internet. AffectNet serves as the largest database of facial expressions, valence, and arousal represented in two different emotion models. With the help of evaluation metrics, deep neural network baselines can perform better than the conventional learning methods [2].