Static posed versus genuine smile recognition

Recognition of a posed or fake smile is a vital and challenging research topic and a growing interest has been observed from the computer vision and machine learning community. The state-of-the-art algorithms related to this field focus on the facial expressions dynamics, while several psychologists suggest that the main difference between posed and spontaneous smile should be observed in different muscles contractions in the upper part of the face. Therefore, in this work we evaluate the accuracy of recognition based only on the face appearance using the High-Dimensional Local Binary Patterns. The smile authenticity is analyzed on the set of images extracted at the smile apex phase from the UvA-NEMO database. The obtained results indicate that the analyzed algorithms can spot a fake smile much better than a human, but worse than systems that incorporate the facial dynamics.