Smile detection using Pair-wise Distance Vector and Extreme Learning Machine

A smile is a common human facial expression used as the indicator for positive emotion. The detection of smiling has many applications, for example, controlling camera shutter when a smile is detected and measuring the degree of satisfaction during a video conference. Many feature extraction methods have been proposed for detecting a smile in the unconstrained scenarios. However, the dimensions of most existing feature descriptors are too huge, which limits their real applications. Moreover, features should be more effective to distinguish between smile face and non-smile face. Motivated by the observation that the mouth shape can effectively reflect a person's smile state, we extracted a novel and snappy set of features that form a feature vector named Pair-wise Distance Vector, which is calculated only based on few points around a mouth. After that, Extreme Learning Machine (ELM) is adopted to classify smile based on these features. The experimental results on GENKI-4K database show that our proposed method outperforms the state-of-the-art methods in terms of accuracy and dimension of features.