Feature Extraction via Recurrent Random Deep Ensembles and its Application in Gruop-level Happiness Estimation
This is an incremental improvement for computer vision researchers working on emotion estimation in group photos.
The paper tackled group-level happiness intensity prediction from images by proposing a recurrent random deep ensemble framework to extract discriminative features, achieving a 0.55 RMSE on the HAPPEI dataset validation set, significantly better than the 0.78 baseline.
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.