A Deep Learning Approach for Face Detection and Location on Highway

Face detection and location technique is a hot research direction during recent years. Especially, driver face detection on highway is still a challenging problem in social safty deserving research. This paper proposes a novel algorithm based on the improved Multi-task Cascaded Convolutional Networks (MTCNN) and Support Vector Machine (SVM) to realize accurate face region detection and feature location of driver's face on highway, predicting face and feature location via a coarse-to-fine pattern. The proposed algorithm is verified under various complex highway conditions. Experimental results show that the proposed model shows satisfied performance compared to other state-of-the-art techniques used in driver face detection and alignment, keeping robust to the occlusions, varying pose and extreme illumination on highway.