Lightweight Multilayer Random Forests for Monitoring Driver Emotional Status

Abstract

This study proposes a lightweight multilayer random forest (LMRF) model, which is a non-neural network style deep model consisting of layer-by-layer random forests. Although a deep neural network (DNN) is a powerful algorithm for facial expression recognition (FER), the requirement of too many parameters, careful parameter tuning, a huge amount of training data, black-box models, and a pre-trained architecture remain significant burdens for a current DNN, particularly for real-time processing. To overcome the limitations of a DNN, our FER system uses LMRF consisting of a two-layer structure and a small number of trees per layer for fast FER. The proposed LMRF achieves a similar performance as a DNN even with a small number of hyper-parameters, and a faster processing time using a CPU. We conducted experiments using a benchmark database captured indoors and a real driving database captured using a near-infrared (NIR) camera. Based on a performance evaluation against a few other state-of-the-art FER methods, the proposed method showed a more uniform performance than DNN-based methods, and required a reduced number of parameters and operations without a loss of accuracy when compared to DNN model compression algorithms. As a replacement for deeper and wider networks, the proposed model can be embedded in low-power and low-memory in vehicle systems for the monitoring of a driver's emotion.

Keywords

• Non-neural networks
• deep random forest
• driver emotion monitoring
• facial expression recognition
• multilayer random forest