本科毕业设计（论文）
基于人脸识别的疲劳驾车检测系统的研究
学院（系）：电气工程学院         
专    业：测控技术与仪器                                   
学生 姓名：                                 
学    号：           
指导 教师：                                   
答辩 日期：                                 
摘要
近些年由于人们的生活节奏快，工作压力大，因疲劳问题而引起的事故时有发生，疲劳驾驶而酿成的惨剧更是使我们警钟长鸣。为了解决这个问题，本次毕业设计着重讨论一种基于ASM模型的人脸识别算法和一种疲劳驾驶的面部特征判定标准。本文的主要内容是：
首先，简要介绍此次研究的课题意义，再介绍人脸识别和疲劳检测的一些相关的背景知识，由于此次课题是疲劳驾驶的检测，所以介绍的重点是关于疲劳驾驶的检测研究。
其次，讨论疲劳的判定方法，这一部分主要是对疲劳特征的选择研究，涉及到人眼睛和嘴的形状的疲劳特征分析，然后进行疲劳分析的综合。
然后，详细介绍ASM模型的定义，在这一部分中还介绍了ASM的算法、区域收敛方法以及判定收敛的依据，另外，在这部分中将给出算法实现步骤的流程图。
再后，主要内容是图像处理与识别，其中包括图像的预处理和手工描点方法所需要确定的参量，以及如何用matlab程序得到这些参量。程序的流程图也在这一章展示。
关键词　人脸识别；疲劳检测；疲劳特征；动态形状模型
   
Abstract
Recently as the fast pace of life and heavy work pressure, accidents caused by fatigue happened frequently. Traffic accident disaster is causing the bells to ring. To solve this problem, the graduation project focused on a face recognition algorithm based on ASM model and a facial features determination standards of fatiguedriving. The main contents are:
First, there is a brief introduction of the research, the relevant background knowledge of 超详细的学开车步骤face recognition and fatigue testing will be given later. as it is the detection of fatiguedriving that we are taking care of, we will mainly focus on the it.
Secondly, we will learn the judging methods of fatigue, the part choice of the fatigue characteristics is studied here, the fatigue characteristics of eyes and mouth is analyzed, then we will comprehensively analysis them.
Then, the principle and method of ASM person face recognition technology is studied. In t
his section, we will describe the ASM algorithm, the covergence method and the basis of determining convergence.The flow chart of algorithm will also be given flow chart in this part.
Finally, we will show picture pre-treatment methods and picture recognition, this part includes the image pre-processing, the needed parameters and how to use the matlab program to get the parameters. The idea and flow chart of the main program will be given.
Keywords　Face Recognition; Fatigue Detection; Fatigue Characteristics; Active Shape models