Smile Detection in the Wild Based on Transfer Learning

In this work, we propose an efficient transfer learning-based smile detection approach to leverage the large amount of labeled data from face recognition datasets and to alleviate over fitting on smile detection. Smile detection from unconstrained facial images is a specialized and challenging problem. As one of the most informative expressions, smiles convey basic underlying emotions, such as happiness and satisfaction, and leads to multiple applications, such as human behavior analysis and interactive controlling.

Compared to the size of databases for face recognition, far less labeled data is available for training smile detection systems. A well-trained deep face recognition model is explored and fine-tuned for smile detection in the wild, unlike previous works which use either hand-engineered features or train deep Convolutional Networks from scratch. 

Three different models are built as a result of fine-tuning the face recognition model with different inputs, including aligned, unaligned and gray scale images generated from the GENKI-4K data set. Experiments show that the proposed approach achieves improved state-of-the art performance. Robustness of the model to noise and blur artifacts is also evaluated in this paper.