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研究生： 陳美琪
Mei Chi, Chen
論文名稱： 基於二階層式支持向量機之即時注視區域分析
Two-Layer SVM for Real-Time Gaze Estimation
指導教授： 李忠謀
Lee, Chung-Mou
學位類別： 碩士
Master
系所名稱： 資訊工程學系
Department of Computer Science and Information Engineering
論文出版年： 2014
畢業學年度： 102
語文別： 中文
論文頁數： 58
中文關鍵詞： 人眼偵測 、注視區域分析 、支持向量機
英文關鍵詞： Eye detection, Gaze estimation, SVM
論文種類： 學術論文
相關次數： 點閱：189  下載：5 
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    • 眼動追蹤過去經常被使用在學術研究方面，近年來由於技術的進步眼動追蹤也被應用在醫療以及交通方面，抑或是應用於駕駛或課堂學生專注度的分析等。然而，過去相關的研究技術許多會利用侵入性的紅外線設備照射眼睛，或是利用較為昂貴的眼動儀輔助，雖然可提高注視點分析辨識率及準確度，但卻忽略了對人體可能的潛在傷害或是無法為大眾輕易取得的缺點。
    本研究提出一個使用筆記型電腦內建之低解析度的網路攝影機即時偵測眼睛與注視點分析方法，實現以低成本且可輕易取得之設備達到正確偵測眼睛與注視點分析的目的。本研究主要方法分成兩大部分，首先利用Adaboost的人臉及人眼偵測獲得眼睛影像，接著加入光線濾波，利用眼睛區域平均灰階值過濾過強的光線，並且記錄使用者的眼睛特徵資訊（包含眼睛開合高度、上眼瞼斜率以及瞳孔位置）；其次記錄使用者於不同注視區塊的眼睛資訊，透過本論文提出之二階層式支持向量機（2-Layer Support Vector Machine），建構使用者相對於當下環境的注視點模型，藉由比對測試資料及模型資訊以達到注視區塊的決策。
    注視區塊決策準確度在注視輔助點固定的狀況下平均可達84%，比使用單一層支持向量機之準確度高出9.4%，而在注視輔助點是隨機出現的情況下平均約為80%。

    Eye-gaze estimation has been used in attention analysis and human behavior research. Eye tracker has been an expensive tool used in these researches. In recent resent researches, intrusive and non-intrusive methods for eye-gaze estimation have been proposed to replace eye tracker. In this research, we proposed a non-intrusive real-time gaze estimation system using webcam as input device.
    A 2-layer support vector machine (SVM) is proposed to determine the human eye gaze region. We had 9 gaze regions within the monitor, which was divided into 3×3 grids. Two types of eye features were used in the 2-layer SVM: 1st layer SVM applied shape type features (eye height and eyelid slope) to determine which row the gaze located, and 2nd layer applied location type features (pupil location) to finally determine the gaze region.
    Experiments with 7 subjects showed an 84% average accuracy on fixed-point gaze estimation, and 80% average accuracy on random-point gaze estimation.

    摘要 i Abstract ii 致謝 iii 目錄 iv 圖目錄 vii 表目錄 ix 第一章 前言 1 1.1研究動機 1 1.2 研究目的 1 1.3 研究範圍與限制 2 1.4 論文架構 3 第二章 文獻探討 4 2.1人臉偵測 4 2.1.1 Knowledge-Based Approach 6 2.1.2 Feature Invariant Approach 6 2.1.3 Template Matching Approach 7 2.1.4 Appearance-Based Approach 8 2.2 人眼偵測 9 2.2.1 Shape-Based Approaches 9 2.2.2 Feature-Based Shape Methods 9 2.2.3 Appearance-Based Methods 10 2.2.4 Hybrid Models 10 2.3瞳孔偵測 11 2.3.1 Template Matching 11 2.3.2 Feature-Based Approach 11 2.4 注視點分析 13 2.4.1 Appearance-Based Methods 13 2.4.2 Feature-Based Methods 13 2.4.3 Neural Network 14 第三章 研究方法 15 3.1 研究目標 15 3.2 系統架構 16 3.3 前處理（PREPROCESSING） 18 3.3.1 灰階影像轉換 18 3.3.2 伽馬校正 19 3.4 人臉偵測（FACE DETECTION） 19 3.4.1 積分影像（Integral Image） 20 3.4.2 矩形特徵（Haar-Feature） 21 3.4.3 階層式分類器（Cascade Classifier） 22 3.5 人眼偵測（EYE DETECTION） 23 3.6 眼睛特徵擷取（EYE FEATURE EXTRACTION） 24 3.6.1 光線濾波器（Lighting Filter） 25 3.6.2 瞳孔偵測（Pupil Detection） 26 3.6.3 OTSU演算法 28 3.6.4 眼睛開合幅度 31 3.7 注視區域分析（GAZE ESTIMATION） 32 3.7.1 支持向量機（Support Vector Machine, SVM） 33 3.7.2 基於二階層式支持向量機（2-Layer SVM）之注視區域決策 34 第四章 實驗結果與分析 39 4.1 程式開發環境 39 4.2實驗環境 39 4.3人眼偵測實驗結果 40 4.4 瞳孔偵測實驗結果 42 4.4.1實驗一：以CASIA資料庫評估瞳孔偵測演算法準確度 42 4.4.2實驗二：以系統實際擷取的眼睛影像評估瞳孔偵測準確度 45 4.5 注視區域分析實驗結果 47 4.5.1實驗一：第一層支持向量機（1st SVM）準確度驗證 48 4.5.2實驗二：比較使用單一支持向量機及二階層式支持向量機之實驗結果 49 4.5.3實驗三：注視輔助點隨機產生之二階層式支持向量機決策準確度 51 第五章 結論及未來研究 54 5.1結論 54 5.2應用 54 5.3未來研究 55 參考文獻 56

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