Abstract:

The smile is the most common and frequently expressed facial expression. Human beings have evolved ways to fake smiles, and developed the ability to detect the disguise. Dynamic information of facial movements has been shown to be essential in both expressing and recognition of facial expressions. The underlying dynamic information of smiles may be investigated to better differentiate between fake and genuine smiles. With the aids of feature extraction methods from Computer Vision, we investigate the dynamic features of smiles (such as the duration, direction, velocity, smoothness, dynamic symmetry, and synchronicity and head motion patterns). Furthermore, we investigate how various dynamic features of genuine and fake smiles may differ across different situations to further our understanding of the human smile. We also aim to investigate the underlying mechanism of individual's ability to distinguish between genuine and fake smiles. We examined whether paying attention to effective dynamic features would help improve recognition performance, and test the perception-attention hypothesis. By comparing the features of smiles and examining the underlying mechanism behind people’s ability to recognize the veracity of smiles. We can obtain a better understanding of the relationship between encoding and decoding of the facial expressions.

Key words: facial expression, smile, facial expression recognition, dynamic features, disguise