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.
This project proposes an efficient transfer learning-based smile detection approach to leverage the large amount of labeled data from face recognition datasets and to alleviate overfitting on smile detection. 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 grayscale images generated from the GENKI-4K dataset. 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 project.