Biblio

Emotions are a powerful tool in communication and one way that humans show their emotions is through their facial expressions. One of the challenging and powerful tasks in social communications is facial expression recognition, as in non-verbal communication, facial expressions are key. In the field of Artificial Intelligence, Facial Expression Recognition (FER) is an active research area, with several recent studies using Convolutional Neural Networks (CNNs). In this paper, we demonstrate the classification of FER based on static images, using CNNs, without requiring any pre-processing or feature extraction tasks. The paper also illustrates techniques to improve future accuracy in this area by using pre-processing, which includes face detection and illumination correction. Feature extraction is used to extract the most prominent parts of the face, including the jaw, mouth, eyes, nose, and eyebrows. Furthermore, we also discuss the literature review and present our CNN architecture, and the challenges of using max-pooling and dropout, which eventually aided in better performance. We obtained a test accuracy of 61.7% on FER2013 in a seven-classes classification task compared to 75.2% in state-of-the-art classification.

Fuzzy density is an important part of fuzzy integral, which is used to describe the reliability of classifiers in the process of fusion. Most of the fuzzy density assignment methods are based on the training priori knowledge of the classifier and ignore the difference of the testing samples themselves. To better describe the real-time reliability of the classifier in the fusion process, the dispersion of the classifier is calculated according to the decision information which outputted by the classifier. Then the divisibility of the classifier is obtained through the information entropy of the dispersion. Finally, the divisibility and the priori knowledge are combined to get the fuzzy density which can be dynamically adjusted. Experiments on JAFFE and CK databases show that, compared with traditional fuzzy integral methods, the proposed method can effectively improve the decision performance of fuzzy integral and reduce the interference of unreliable output information to decision. And it is an effective multi-classifier fusion method.