Dimpler Detection Using Facial Landmarks in Videos

Dimpler is one of the body languages that contributes to the emotion contempt when the action appears unilaterally, and to boredom. It is one of the subtle expressions that people did in everyday life. Although the universal seven microexpressions are clear signs of concealed emotions, subtle expressions such as dimple probably occur much more frequently than universal expressions. The dimpler muscle pulls the lip corners to the side and creates a dimple in the cheek. This study developed a dimpler detection model using 2D facial landmarks. 3 videos of totally 6 minutes were recorded while each video involved dimple and non-dimple expressions. Cheek and lip landmark were detected from each frame of the video using the Face-Alignment facial landmark detector. Features such as horizontal lip distance, vertical lip distance and lip ratio served as inputs to a linear Support Vector Machine (SVM) model. The SVM approach achieved a performance of accuracy 82.37%, sensitivity 86.58% and specificity of 84.29%.The results suggest that horizontal lip distance, vertical lip distance and lip ratio are useful features for the detection of dimpler in videos