Cascade learning for driver facial monitoring

Chao Gou, Yuchen Zhou, Yao Xiao, Xiao Wang, Hui Yu

Research output: Contribution to journalArticlepeer-review

38 Downloads (Pure)


As a non-invasive method, vision-based driver monitoring aims to identify risky maneuvers for intelligent vehicles and it has gained an increasing interest over recent years. However, most existing methods tend to design models for specific tasks, such as head pose or gaze estimation, which results in redundant models hampering real time applications. Besides, most driver facial monitoring methods ignore the correlation of different tasks. In this work, we propose a unified framework based on cascade learning for simultaneous facial landmark detection and head pose estimation, as well as simultaneous eye center detection and gaze estimation. In particular, built upon the key idea that facial landmark locations and 3D face model parameters are implicitly correlated, we introduce a cascade regression framework to achieve these two tasks simultaneously. After coarsely extracting the driver's eye region from the detected facial landmarks, we perform cascade regression for simultaneous eye center detection and gaze estimation. Leveraging the power of cascade learning allows our method to alternatively optimize facial landmark detection, head pose estimation, eye center localization, and gaze prediction. The comparison experiments conducted on benchmark datasets of 300-W, GI4E, BU, MPIIGaze, and driving dataset of SHRP2 demonstrate that our proposed method can achieve state-of-the-art performance with robust effectiveness on the real driver monitoring applications.

Original languageEnglish
Pages (from-to)1-10
Number of pages10
JournalIEEE Transactions on Intelligent Vehicles
Early online date10 May 2022
Publication statusEarly online - 10 May 2022


  • cascade learning
  • driver monitoring
  • faces
  • facial landmark detection
  • gaze estimation
  • head pose estimation
  • intelligent vehicles
  • monitoring
  • pose estimation
  • predictive models
  • task analysis
  • three-dimensional displays


Dive into the research topics of 'Cascade learning for driver facial monitoring'. Together they form a unique fingerprint.

Cite this