Landmark-free smile intensity estimation

Facial expression analysis is an important field of research, mostly because of the rich information faces can provide. The majority of works published in the literature have focused on facial expression recognition and so far estimating facial expression intensities have not gathered same attention. The analysis of these intensities could improve face processing applications on distinct areas, such as computer assisted health care, human-computer interaction and biometrics. Because the smile is the most common expression, studying its intensity is a first step towards estimating other expressions intensities. Most related works are based on facial landmarks, sometimes combined with appearance features around these points, to estimate smile intensities. Relying on landmarks can lead to wrong estimations due to errors in the registration step. In this work we investigate a landmark-free approach for smile intensity estimation using appearance features from a grid division of the face. We tested our approach on two different databases, one with spontaneous expressions (BP4D) and the other with posed expressions (BU-3DFE); results are compared to state-of-the-art works in the field. Our method shows competitive results even using only appearance features on spontaneous facial expression intensities, but we found that there is still need for further investigation on posed expressions. Keywords-Smile intensity estimation; Facial expression analysis; Feature extraction; Machine learning;