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.
Facial expression analysis usually employs a three-stage training consisting of feature learning, feature selection, and classifier construction. These features can be either hand-designed or learned from training images. Then, a subset of the extracted features, which is the most effective to distinguish one expression from the others, is selected to facilitate an efficient classification and enhance the generalization capability. Finally, a classifier is constructed given the extracted feature set for each target facial expression. Recently, it has been demonstrated that expression recognition can benefit from performing these three stages together with certain multi-layer (i.e., deep) neural network architectures. As a result, the majority of deep learning techniques applied for facial expression recognition ”in-the-wild” revolve around learning static discriminative templates via deep convolutional neural networks (DCNN) and using score aggregation for video classification to universal expressions @cite_3 .
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A training process for facial expression recognition is usually performed sequentially in three individual stages: feature learning, feature selection, and classifier construction. Extensive empirical studies are needed to search for an optimal combination of feature representation, feature set, and classifier to achieve good recognition performance. This paper presents a novel Boosted Deep Belief Network (BDBN) for performing the three training stages iteratively in a unified loopy framework. Through the proposed BDBN framework, a set of features, which is effective to characterize expression-related facial appearance shape changes, can be learned and selected to form a boosted strong classifier in a statistical way. As learning continues, the strong classifier is improved iteratively and more importantly, the discriminative capabilities of selected features are strengthened as well according to their relative importance to the strong classifier via a joint fine-tune process in the BDBN framework. Extensive experiments on two public databases showed that the BDBN framework yielded dramatic improvements in facial expression analysis.