Stacking-based deep neural network for Facial Expression Recognition

We present a scalable stacking-based deep neural network(S-DNN) for facial expression recognition. The network is a congregate of basic learning models in series to synthesize a deep neural network with feedforward network architecture. Thur, choosing trainable learning modules is the core to effectively build S-DNN in an end-to-end manner. Inspired by the manifold learning archetype, we implement a Patch Discriminative Analysis(PDA) as a basic learning model, followed by hashing and block histogram on the top, which sample image in a low discriminative space, and finding an efficient representation of the training data. As those self-learnable models trained, a low dimensional discriminative feature is implicitly learned, which proves to be useful in facial expression recognition. Experimental results on the facial expression dataset(CK+) show that the proposed model is superior to its counterparts, capable of achieving state-of-the-art performance.