Hybrid hypergraph construction for facial expression recognition
Yuchi Huang, Hanqing Lu · 2016
In this paper, we proposed a novel framework for facial expression recognition, in which face images were taken as vertices in a hypergraph and the task of expression recognition was formulated as the problem of hypergraph based inference. A hybrid strategy was developed to construct hyperedges: we generated probabilities of facial action units by deep convolutional networks and took each action unit as an ‘attribute’ to represent a hyperedge; we also formed hyperedges by using embedded network features before the last full connected layer to perform local clustering. In this way, each face image was assigned to various hyperedges by exploiting the representational power of deep convolutional networks. Our facial expression recognition system generates expression labels by a hypergraph based transductive inference approach, which tends to assign the same label to vertices that share many incidental hyperedges, with the constraints that predicted labels of training images should be similar to their ground truth labels. We compared the proposed approach to state-of-the-art methods and its effectiveness was demonstrated by extensive experimentation.