Intelligent Driver Emotion Monitoring Based on Lightweight Multilayer Random Forests

Abstract

In this paper, we propose a lightweight multi-layer random forest (LMRF) model. The LMRF model is a non-neural network-style deep model composed of arbitrary forests rather than layers. DNN is a powerful algorithm for facial recognition (FER), but there are too many parameters, careful parameter tuning, large amounts of training data, black box models, and pretrained architecture required for a current DNN. To overcome the burden of real-time processing DNN, we use the proposed LMRF with two tree structures per layer and a small number of trees for high-speed FER. We conducted experiments using an actual driving database captured using a near-infrared (NIR) camera to monitor the driver's emotions. The proposed LMRF provides similar FER accuracy to DNN with a small number of hyperparameters, and the faster processing time using the CPU.