Smile recognition based on deep Auto-Encoders

Most of smile recognition methods are based on constrained databases. Thus there are a lot of limitations when applying those algorithms into the real-world smile recognition. For the purpose of improving the accuracy in real-world smile recognition, we conducted our experiments on two databases (GENKI-4K database and our own built database). Depending on deep learning theory, we constructed a new deep model by stacking Contractive Auto-Encoder (CAE) on Contractive Denoising Auto-Encoder (CDAE) to extract useful features. Firstly, we pre-trained a CDAE to extract the feature of the first layer, then the extracted feature were used as input of the next basic model CAE, by pre-training the CAE model, we got more abstract feature, then the feature were used to classification. Experiments showed that our approach was useful for smile recognition. On the other hand, we also explored the influence of different number of training samples.