A Deep Learning Approach for Face Detection and Location on Highway

2018 2nd International Conference on Artificial Intelligence Applications and Technologies

计算机科学

Zhang, Yang^1,2 ; Lv, Peihua^1,2 ; Lu, Xiaobo^1,2

School of Automation, Southeast University, Nanjing

210096, China^1

Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, Southeast University, Nanjing

210096, China^2

关键词: Coarse to fine;    Convolutional networks;    Extreme illuminations;    Feature location;    Learning approach;    Location techniques;    Novel algorithm;    State-of-the-art techniques;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/435/1/012004/pdf
DOI  :  10.1088/1757-899X/435/1/012004

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

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.