Automatic Hidden Sadness Detection Using Micro-Expressions

Micro-expressions (MEs) are very short, rapid, difficult to control and subtle which reveal hidden emotions. Spotting and recognition of MEs are very difficult for humans. Lately, researchers have tried to develop automatically MEs detection and recognition algorithms, however the biggest obstacle is the lack of a suitable datasets. Previous studies mainly focus on posed rather than spontaneous videos, and the obtained performances were low. To address these challenges, firstly we made a hidden sadness database, which includes 13 video clips elicited from students, who were watching very sad scenes from the movie in the University environment. Secondly, a new approach for automatic hidden sadness detection algorithm is proposed. Finally, Support Vector Machine and Random Forest classifiers are applied, since it has been shown that they provide state-of-the-art accuracy for the facial expression recognition problem. Two experiments were conducted, one with all extracted features from the face, and the other with only eye region features. The best results are achieved with Random Forest algorithm using all face features, with the recognition rate of 95.72%. For further improvement of the performance, we plan to integrate the deep Convolutional Neural Network algorithm, due to its grow popularity in the visual recognition.