Current advances in driver drowsiness detection consist of a variety of innovative technologies generally based on driver state monitoring systems. Extracting effective and relevant features to characterize drowsy symptoms in images and videos is still an open topic. In this work, we introduce a face monitoring system based on a compact face texture descriptor able to cover the most discriminant drowsy features. The compactness has been achieved by both a multi-scale pyramidal face representation that capture the main characteristics of local and global information, and the feature selection process applied on the raw extracted features. The proposed framework is rolled out in four phases: (i) face detection and alignment; (ii) Pyramid-Multi Level (PML) face representation; (iii) face description using a multi-level multi scale feature extraction; and (vi) feature subset selection and classification. Experiments conducted on the public dataset NTH Drowsy Driver Detection (NTHUDDD) show the effectiveness of the proposed face descriptor and the associated selection schemes. The results show that the proposed method compares favorably with several approaches including those based on deep Convolutional Neural Networks.

驾驶员睡意检测的最新进展通常由各种基于驾驶员状态监视系统的创新技术组成。提取有效和相关的特征以表征图像和视频中的困倦症状仍然是一个开放主题。在这项工作中，我们介绍了一种基于紧凑型人脸纹理描述符的人脸监控系统，该描述符能够涵盖最明显的困倦特征。通过捕获本地和全局信息主要特征的多尺度金字塔面部表示以及应用于原始提取特征的特征选择过程，实现了紧凑性。提议的框架分四个阶段推出：（i）人脸检测和对齐；（ii）金字塔多层次（PML）人脸表示；（iii）使用多级多尺度特征提取进行人脸描述；（vi）特征子集的选择和分类。在公共数据集NTH昏昏欲睡驾驶员检测（NTHUDDD）上进行的实验表明，所提出的面部描述符和相关选择方案的有效性。结果表明，所提出的方法与包括基于深度卷积神经网络的方法在内的多种方法相比具有优势。