Driving Fatigue Recognition Based on  Higher-order Singular Value Decomposition

doi:10.3969/j.issn.1000-3428.2011.15.045

Computer Engineering ›› 2011, Vol. 37 ›› Issue (15): 143-145. doi: 10.3969/j.issn.1000-3428.2011.15.045

• Networks and Communications • Previous Articles Next Articles

Driving Fatigue Recognition Based on Higher-order Singular Value Decomposition

HUANG Wei ^1,2, ZHANG Wei ¹, XIA Li-min ¹

(1. College of Information Science and Engineering, Central South University, Changsha 410075, China; 2. Department of Computer and Information Engineering, Changsha Aeronautical Vocational and Technical College, Changsha 410014, China)

Received:2011-03-04 Online:2011-08-05 Published:2011-08-05

基于高阶奇异值分解的驾驶疲劳识别

黄炜^1,2，张伟¹，夏利民¹

(1. 中南大学信息科学与工程学院，长沙 410075；2. 长沙航空职业技术学院计算机系，长沙 410014)

作者简介:黄炜(1972－)，女，讲师、硕士研究生，主研方向：模式识别；张伟，讲师、博士；夏利民，教授、博士、博士生导师
基金资助:
国家博士点基金资助项目(20090162110057)；湖南省自然科学基金资助项目(05JJ30121)

Abstract

Abstract: This paper presents a new method for fatigue recognition based on Higher-order Singular Value Decomposition(HOSVD). Facial velocity information, which is determined using optical flow techniques, is used to characterize fatigue. In order to remove the influence of identity, head poses, and lighting conditions, HOSVD is used to extract fatigue features. Experimental results show that the proposed method is effective and attains a satisfactory effect.

Key words: fatigue recognition, fatigue features, optical flow, Higher-order Singular Value Decomposition(HOSVD), tensor, K Nearest Neighbor (KNN) method

摘要： 提出一种基于人脸运动特征和高阶奇异值分解的驾驶疲劳识别方法。利用光流技术计算人脸皮层的运动速度，以此作为疲劳特征。为消除身份、光照和姿态等因素对疲劳识别的影响，利用高阶奇异值分解将疲劳特征与身份信息、光照信息、姿态信息分离。在疲劳子空间采用余弦距离最近邻方法进行疲劳识别。对不同光照条件下、不同人、不同姿态的疲劳状态进行识别实验，实验结果表明，该方法具有较好的识别效果。

关键词: 疲劳识别, 疲劳特征, 光流, 高阶奇异值分解, 张量, K最近邻法

CLC Number:

TP391.41

HUANG Wei, ZHANG Wei, JIA Li-Min. Driving Fatigue Recognition Based on Higher-order Singular Value Decomposition[J]. Computer Engineering, 2011, 37(15): 143-145.

黄炜, 张伟, 夏利民. 基于高阶奇异值分解的驾驶疲劳识别[J]. 计算机工程, 2011, 37(15): 143-145.

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