本文選題：眼動特征　切入點：眼瞼匹配　出處：《北京交通大學》2017年碩士論文

【摘要】：眼睛是我們最重要的特征之一,我們通過眼睛來獲取外界信息。眼動特征研究在疲勞駕駛、認識視覺工作的原理、分析人的情感及行為、基于眼動的人機交互等問題上都發(fā)揮著關鍵作用�；谝曈X的眼動特征技術具有易操作性、低損失、高準確度等優(yōu)勢,目前是眼動研究的主要方向。在過去的幾十年中,眼動特征研究雖然取得了顯著的進步,但仍有很多不可控制的因素,影響檢測結果。每個人的眼睛形狀和大小都不同,眼瞼遮擋眼睛的面積也不一樣,位置和光照條件變化的不同,睫毛等噪聲的干擾,都會直接影響到眼動特征研究的準確性,市面上單純采集眼部數(shù)據設備價格非常昂貴且數(shù)量稀少,大部分的眼動特征研究都是建立在人臉檢測的基礎上,直接對眼睛進行檢測和特征分析十分稀缺。鑒于這些問題,本文集中研究了眼瞼和瞳孔兩部分眼動特征。首先提出了一個基于ASM算法和Kalman濾波的人眼檢測模型——AK-EYE模型,并應用該模型對實時眼睛形狀和位置跟蹤算法進行改進,提高眼瞼匹配的速度和精度;然后,通過模板匹配技術來定位瞳孔,并專門采集眼部數(shù)據集進行驗證實驗;最后,結合眼動特征對疲勞檢測方法進行實驗分析。主要內容如下:(1)詳細分析了相關的眼動特征研究方法的主要思想及其優(yōu)缺點,在眼瞼匹配和定位過程中,針對ASM算法中出現(xiàn)的匹配位置不準確的問題,提出了結合ASM算法和Kalman濾波的AK-EYE模型,并利用該模型對眼瞼進行匹配。首先,按照特定標準選取合適的樣本特征進行標定,建立眼瞼形狀模型,然后,對眼瞼輪廓進行搜索匹配,預測和更新模型的初始位置,完成實時的眼瞼定位跟蹤,并分析實驗結果,驗證算法的有效性。(2)針對包含人臉的數(shù)據集和單純眼部的數(shù)據集,選擇合適的模板,利用模板匹配算法實現(xiàn)瞳孔的精確定位,并分別進行實驗結果展示和分析。對不同的模板匹配算法性能進行比較,并將本文模板匹配算法在公共數(shù)據庫上進行實驗驗證。(3)根據眼動特征研究得到的眼瞼信息和瞳孔信息,建立疲勞預警系統(tǒng)。該疲勞預警系統(tǒng)使用眼瞼特征信息和瞳孔特征信息作為疲勞狀態(tài)的判斷輸入參數(shù),并將這些眼動特征和PERCLOS測定原理結合,構建新的疲勞分析判斷算法,判斷疲勞狀態(tài)。
[Abstract]:The eye is one of our most important features. We use our eyes to obtain information about the outside world. Eye movement features research fatigue driving, understanding the principles of visual work, and analyzing people's emotions and behaviors. Eye movement feature technology, which has the advantages of easy operation, low loss, high accuracy and so on, is the main research direction of eye movement research in the past few decades. Although significant progress has been made in the study of eye movement characteristics, there are still many uncontrollable factors that affect the test results. Each person's eyes are different in shape and size, and the area of eyelid occlusion is different. The difference of position and illumination condition, the interference of eyelash and other noise will directly affect the accuracy of the study of eye movement characteristics. The price of the simple collection of eye data is very expensive and the quantity is scarce. Most of the research on eye movement feature is based on face detection. It is very rare to directly detect and analyze the eye features. In view of these problems, This paper focuses on the eye movement characteristics of eyelid and pupil. Firstly, an eye detection model AK-EYE based on ASM algorithm and Kalman filter is proposed, and the real-time eye shape and position tracking algorithm is improved by this model. Improve the speed and accuracy of eyelid matching; then, through template matching technology to locate the pupil, and special collection of eye data set for verification experiment; finally, The main contents are as follows: (1) the main ideas and advantages and disadvantages of the related methods are analyzed in detail. In the process of eyelid matching and locating, In order to solve the problem of inaccurate matching position in ASM algorithm, a AK-EYE model combining ASM algorithm and Kalman filter is proposed, and the model is used to match eyelids. Firstly, appropriate sample features are selected to calibrate according to specific criteria. The eyelid shape model is established, then the eyelid contour is searched and matched, the initial position of the model is predicted and updated, the real time eyelid location tracking is completed, and the experimental results are analyzed. To verify the validity of the algorithm, we select the appropriate template for the dataset containing the face and the simple eye data set, and use the template matching algorithm to locate the pupil accurately. The performance of different template matching algorithms is compared, and the template matching algorithm of this paper is tested on the common database to verify the eyelid information and pupil information obtained from the study of eye movement characteristics. A fatigue early warning system is established, which uses eyelid characteristic information and pupil characteristic information as input parameters to judge fatigue state, and combines these eye movement characteristics with the principle of PERCLOS measurement to construct a new fatigue analysis and judgment algorithm. Judge the fatigue state.
【學位授予單位】：北京交通大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41

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