  With the progress in automatic human behavior understanding, analysing the
perceived affect of multiple people has been recieved interest in affective
computing community. Unlike conventional facial expression analysis, this paper
primarily focuses on analysing the behaviour of multiple people in an image.
The proposed method is based on support vector regression with the combined
global alignment kernels (GAKs) to estimate the happiness intensity of a group
of people. We first exploit Riesz-based volume local binary pattern (RVLBP) and
deep convolutional neural network (CNN) based features for characterizing
facial images. Furthermore, we propose to use the GAK for RVLBP and deep CNN
features, respectively for explicitly measuring the similarity of two
group-level images. Specifically, we exploit the global weight sort scheme to
sort the face images from group-level image according to their spatial weights,
making an efficient data structure to GAK. Lastly, we propose Multiple kernel
learning based on three combination strategies for combining two respective
GAKs based on RVLBP and deep CNN features, such that enhancing the
discriminative ability of each GAK. Intensive experiments are performed on the
challenging group-level happiness intensity database, namely HAPPEI. Our
experimental results demonstrate that the proposed approach achieves promising
performance for group happiness intensity analysis, when compared with the
recent state-of-the-art methods.
