.png){kind=icon}
返回
Efficient and compact face descriptor for driver drowsiness detection
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2020.114334.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
LOCAL BINARY PATTERNS
RECOGNITION
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.5
论文数:
3.0W
被引数:
10.2W
机构
引用论文
Pedestrian Detection and Tracking from Low-Resolution Unmanned Aerial Vehicle Thermal Imagery基于低分辨率无人机热图像的行人检测与跟踪.png)
SENSORS
OAAI
SENSORSIF3.5
OAAI分享收藏Crystalline‐State Reaction with Allosteric Effect in Spin‐Crossover, Interpenetrated Networks with Magnetic and Optical Bistability具有磁和光学双稳态的自旋交叉，互穿网络中具有变构效应的晶态反应
PREAI
分享收藏
查看更多内容