The heterogeneous ensemble of deep forest and deep neural networks for micro-expressions recognition.

Micro-Expressions (MEs) are a kind of short-lived and uncontrollable facial expressions. Therefore, the MEs recognition task poses a great challenge to both the psychological and computer vision research communities. In this study, a new ensemble algorithm is proposed by fusing two different deep learning frameworks: Deep Forest (DF) and Convolutional Neural Networks (CNN) (DFN for short). A modified DF structure is deployed to extract features through the multi-grained scanning technique, along with three different sliding windows to gain diverse motion features. In addition, two shallow CNNs are deployed to extract the features from the three-dimensional optical flow vector and the apex frame. In this way, the fusion of DF and CNNs forms DFN to extract the static and dynamic features for MEs, to generate diverse features with high-level abstraction. Consequently, this heterogeneous ensemble deploys the high diversity in these two models to promote the overall discriminative ability. Comprehensive experiments have confirmed the robustness and effectiveness of the proposed DFN with relatively less computational consumption. Related theoretical analysis has been given to further provide evidence and insights into the proposed method. Our source code is publicly available for non-commercial or research use at https://github.com/MLDMXM2017/DFN_ME. [ABSTRACT FROM AUTHOR]