Research On Key Issues In Computer Vision Based Driver Drowsiness Recognition

Decreased vehicle control due to driver drowsiness is one of the major causes ofroad accidents. Computer vision based methods have shown the possibility ofdrowsiness detection through driver eye movement analysis. Camera monitoring of agiven driver’s eye status has proved to be the most promising technology due to goodaccuracy, real-time performance and non-intrusiveness. However, there are still manychallenges posed by illumination, driver postures and unapparent facial appearancechanges when a driver becomes drowsy. This paper focuses on the key issues in highaccuracy contour extraction methods across illumination and face orientation,orientation estimation and registration, drowsiness feature space modeling anddrowsiness state inferring. Real on-road experiments are performed to testify theaccuracy and robustness of the proposed methods as well.According to a thorough analysis on the influence of dynamic illumination andorientation in eye location, an improved Active Shape Model (ASM) involving twocontributions is introduced for face alignment. First, a novel local texture modelmaximizes the ASM tolerance to illumination changes by learning from theSelf-Quotient image instead of the original image. Second, a cascading overall shapemodel is proposed to enhance ASM orientation adaptability. On the basis of facialfeature alignment using ASM, a more precise eye contour location parameterizationmodel is performed by introducing some illumination insensitive features such aschromaticity information and gradient distribution characteristics. Furthermore, a twolayer cascaded illumination system is presented to eliminate reflections ofglasses. A polarized lighting method is adopted in the first layer, and a doublechannel narrow band multi-spectral imaging system is set up in the secondlayer.Based on the assumption that the features extracted from sequential imagesresemble each other, this paper presents a face detection algorithm which combines alearning-based approach with adaptive skin color segmentation and background modeling methods. The driver’s attitude angles are calculated by tracking corners inthe facial region. Moreover, the initial attitude angles are determined by matching thethree dimensional model of the driver’s head, which is reconstructed from acombination of the Candide model and the driver’s facial image.The changes of each measure with varying drowsiness levels were comparedwith the analysis of variance (ANOVA), and those with statistically significantdifferences are introduced into the drowsiness feature space. Imitating cognitivebehavior of human beings, an identification method is proposed where prioriknowledge obtained from train data sets are used to classify different drowsinessstates during the initial phase of a driving task and then Bayesian networks areintroduced to drowsiness assessment after a period of real-time learning.