Intelligent Driver Emotion Monitoring Based on Lightweight Multilayer Random Forests In this paper, we propose a lightweight multilayer random forest (LMRF) model. The LMRF model is a nonneural networkstyle 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 realtime processing DNN, we use the proposed LMRF with two tree structures per layer and a small number of trees for highspeed FER. We conducted experiments using an actual driving database captured using a nearinfrared (NIR) camera to monitor the driveru0027s emotions. The proposed LMRF provides similar FER accuracy to DNN with a small number of hyperparameters, and the faster processing time using the CPU.

A Lightweight Architecture For Driver Status Monitoring Via Convolutional Neural Networks Nowadays, a significant portion of road accidents is attributed to dirverxe2x80x99s inattention or dangerous behavior such as call or smoke. Monitoring the driverxe2x80x99s status through computer vision techniques to detect driving errors in real time to avoid vehicle collisions is one of the most important issues in machine vision and Advanced Driver Assistant System(ADAS). In this paper, we propose a novel facial landmark model and a lightweight architecture of Driver Status Monitoring(DSM) by integrating several deep learning models in order to monitor the driverxe2x80x99s inattention caused by drowsiness, fatigue and distraction in real time. With inverted residual block and multiscale feature map, the proposed facial landmark model achieve 4.90% normalized mean error on common set of 300W dataset. The proposed detection model achieved a mean average precise(mAP0.5) of 90% on our validation dataset. Several results presented in this paper may provide meaningful insights for the autonomous driving research community and automotive industry for future algorithm development.