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研究生:莊庭杰
研究生(外文):Ting-Chieh Chuang
論文名稱:利用群眾外包機制建立表情基準資料與表情分類器
論文名稱(外文):A Crowdsourcing Mechanism for Building Facial Expression Benchmark Data and Classifiers
指導教授:林信志林信志引用關係
指導教授(外文):Hsin-Chih Lin
口試委員:孫惠民李建億
口試委員(外文):Huey-Min SunChien-I Lee
口試日期:2015-06-29
學位類別:碩士
校院名稱:國立臺南大學
系所名稱:數位學習科技學系碩博士班
學門:教育學門
學類:教育科技學類
論文種類:學術論文
論文出版年:2015
畢業學年度:103
語文別:中文
論文頁數:64
中文關鍵詞:群眾外包表情基準資料臉部表情辨識支持向量機
外文關鍵詞:CrowdsourcingBenchmark DataFacial Expression RecognitionSVM classifier
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群眾外包 (Crowdsourcing) 是透過特定平台,將需要大量人力參與、但電腦難以取代的重複性任務,委託網路上一群不特定的自願者;相較於請專人執行任務,群眾外包可以利用人類本身具備的能力,提升任務品質與效率,並大幅降低成本。本研究提出一套雙系統機制,可以提升表情自動辨識系統正確率,並發展出可運用於其他表情辨識系統的基準資料;雙系統包括:(1) 群眾外包的社群投票系統、(2) 電腦運算的自動辨識系統,社群投票系統是利用人類擅長的表情認知能力,透過群眾外包讓自願者協助分類臉部表情影像,包括:高興、悲傷、驚訝、生氣、害怕、厭惡及中性表情等七種表情類別;為吸引更多自願者參與,本研究將社群投票系統發展成數位遊戲,透過社群投票產生的高辨識度影像,即是表情基準資料,可以訓練/測試任何表情辨識系統;接著,萃取每張基準影像的表情特徵,再將所有的表情特徵向量輸入支持向量機 (Support Vector Machine, SVM) 分類器,但研究發現某些表情特徵十分相近,會造成群眾投票時難以辨識,並影響 SVM 分類器辨識結果,故本研究設計一套二階層 SVM 分類器 (two-level SVM classifier),將表情特徵十分相近的影像合為一群,再個別分類,藉此改善系統正確辨識率。實驗結果顯示:二階層 SVM 分類器能有效提高系統正確辨識率至 95%,且透過社群投票產生的高辨識度影像集,可作為表情基準資料,並用於其他表情辨識系統。
In this study, a crowdsourcing mechanism has been proposed to build a set of benchmark data for training/testing facial expression recognition systems. The proposed mechanism consists of two systems, including a social voting system using a crowdsourcing game and an automatic recognition system by computer algorithms. By our crowdsourcing game, the social voting system calls for a lot of online volunteers to classify facial expression pictures into seven basic emotions, including happiness, sadness, surprise, anger, fear, disgust, and neutral. The classified facial pictures can be used as benchmark data to train/test any facial expression recognition systems. In our automatic recognition system, Luxand FaceSDK is used to extract a nine-dimensional feature vector from each facial picture. These feature vectors are then used in our two-level SVM classifier to recognize facial expressions. Experimental results have showed that the benchmark data built by our crowdsourcing game are highly reliable; meanwhile, our facial expression recognition system can achieve very high accuracy rate.
摘要 1
ABSTRACT 2
目次 3
圖次 5
表次 5
第一章 緒論 7
第一節 研究背景與動機 7
第二節 研究目的 8
第三節 論文架構 9
第二章 文獻探討 10
第一節 群眾外包與相關研究 10
一、 群眾外包 10
二、 人腦計算與相關研究 18
第二節 表情辨識與相關研究 20
一、 基本表情 20
二、 人臉偵測 20
三、 特徵點擷取 21
四、 表情辨識 21
第三章 研究方法 25
第一節 系統架構設計 25
一、 開發環境 25
二、 系統流程 26
第二節 社群投票系統 27
一、 人臉偵測 27
二、 人臉資料庫 33
三、 數位遊戲 35
第三節 自動辨識系統 40
一、臉部特徵點擷取 40
二、表情特徵向量 43
三、支持向量機 46
第四章 實驗結果 47
第一節 實驗設計 47
第二節 實驗結果 47
第三節 表情基準資料 51
第五章 結論與未來展望 53
第一節 結論 53
第二節 未來展望 55
參考文獻 56
中文部份 56
英文部分 57

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