HELPFuL: Human Emotion Label Prediction Based on Fuzzy Learning for Realizing Artificial Intelligent in IoT

Abstract

Human emotion label prediction is crucial to Artificial Intelligent in the Internet of Things (IoT). Facial expression recognition is the main technique to predict human emotion labels. Existing facial expression recognition methods do not consider the compound emotion and the fuzziness of emotion labels. Fuzzy learning is a mathematical tool for dealing with fuzziness and uncertainty information. The advantage of using fuzzy learning for human emotion recognition is that multiple fuzzy sentiment labels can be processed simultaneously. This paper proposes a fuzzy learning-based expression recognition method for human emotion label prediction. First, a fuzzy label distribution system is constructed using fuzzy sets for representing facial expressions. Then, two fuzzy label distribution prediction methods based on fuzzy rough sets are proposed to solve the compound emotion prediction. The probability that a sample is likely and definitely belongs to an emotion is obtained by calculating the upper and lower approximations. Experiments show the proposed algorithm not only performs well on human emotion label prediction but can also be used for other label distribution prediction tasks. The proposed method is more accurate and more general than other methods. The improvement of the method on the effect of emotion recognition extends the application scope of artificial intelligence in IoT.