Riesz-based volume local binary pattern and a novel group expression model for group happiness intensity analysis

Automatic emotion analysis and understanding has received much attention over the years in affective computing. Recently, there are increasing interests in inferring the emotional intensity of a group of people. For group emotional intensity analysis, feature extraction and group expression model are two critical issues. In this paper, we propose a new method to estimate the happiness intensity of a group of people in an image. Firstly, we combine the Riesz transform and the local binary pattern descriptor, named Riesz-based volume local binary pattern, which considers neighbouring changes not only in the spatial domain of a face but also along the different Riesz faces. Secondly, we exploit the continuous conditional random fields for constructing a new group expression model, which considers global and local attributes. Intensive experiments are performed on three challenging facial expression databases to evaluate the novel feature. Furthermore, experiments are conducted on the HAPPEI database to evaluate the new group expression model with the new feature. Our experimental results demonstrate the promising performance for group happiness intensity analysis.