  This paper presents a novel ensemble framework to extract highly
discriminative feature representation of image and its application for
group-level happpiness intensity prediction in wild. In order to generate
enough diversity of decisions, n convolutional neural networks are trained by
bootstrapping the training set and extract n features for each image from them.
A recurrent neural network (RNN) is then used to remember which network
extracts better feature and generate the final feature representation for one
individual image. Several group emotion models (GEM) are used to aggregate face
fea- tures in a group and use parameter-optimized support vector regressor
(SVR) to get the final results. Through extensive experiments, the great
effectiveness of the proposed recurrent random deep ensembles (RRDE) is
demonstrated in both structural and decisional ways. The best result yields a
0.55 root-mean-square error (RMSE) on validation set of HAPPEI dataset,
significantly better than the baseline of 0.78.
