Intensity Estimation of the Real-World Facial Expression

Affect computing or Automatic affect sensing has aroused extensive interests of researchers in the area of machine learning and pattern recognition. Most previous research focused on face detection and emotion recognition while our research explores facial intensity estimation, which cares more about the dynamic changes on a face. CK+ database and Real-world Affective Face Database (RAF-DB) are used to test and implement the algorithms in this paper. To settle the problem of intensity estimation, classification and ranking algorithms are used for training and testing intensity levels. Meanwhile, the performance of five different feature representations is evaluated using the accuracy results obtained from classification approach. By using the optimum feature representation as the input to the next designed training model, ranking results can be attained. Techniques of Learning to Rank in the area of information retrieval are utilized to combat the situation of intensity ranking. RankSVM and RankBoost are used as frameworks to estimate the ranking scores based on sequences of images. The experimental results of scoring are evaluated by the indexes used in information retrieval. Algorithms used in the research are well organized and compared to generate an optimal model for the ranking task.