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研究生:吳政德
研究生(外文):Zheng-De Wu
論文名稱:利用卷積神經網路預測學習情緒之研究
論文名稱(外文):The Study on Recognizing Learning Emotion with the Convolution Neural Networks
指導教授:林冠成林冠成引用關係
指導教授(外文):Kuan-Cheng Lin
口試委員:陳錦杏許志義洪啟舜黃一泓
口試委員(外文):Chin-Hsing ChenJyh-Yih HsuQI-SHUN HONGYi-Hung Huang
口試日期:2017-07-20
學位類別:碩士
校院名稱:國立中興大學
系所名稱:資訊管理學系所
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2017
畢業學年度:106
語文別:中文
論文頁數:47
中文關鍵詞:學習情緒臉部表情辨識機器學習卷積神經網路
外文關鍵詞:Academic EmotionsFacial Expression RecognitionMachine LearningConvolutional Neural Networks
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在教學環境中教師取得學習者的學習狀況的方式除了學習成效之外捕捉學習者的學習情緒也是種有效的方式,因為臉部表情是人類表達情緒的主要方式之一且在人們的情緒交流上發揮了重要的作用,藉由補捉學習情緒給教師可以幫助教師了解學習者的學習狀況。

過去相關研究對於辨識的學習情緒是將基本情緒分為正向情緒與負向情緒或者是理解與不理解之情緒,來詮釋學習者於學習中產生的情緒,然而本研究為了得到更完整的學習情緒,使用學業情緒之分類標籤解釋學習情緒,學習情緒除了能得到學習者的正負向情緒外同時也能得知學習者對於教學內容的反應,因此本研究的重點在於如何辨識學習者的學習情緒。

由於有效的臉部表情特徵設計不易,因此本研究使用深度學習領域中的卷積神經網路(Convolutional Neural Networks, CNN)而非傳統特徵工程方式建立預測模型,CNN是種特殊的網路結構,能夠直接輸入原始影像進行訓練,不需要事先手工設計特徵,因為CNN本身就具有對輸入影像自動學習影像特徵的能力,諸多實驗證明CNN在影像辨識上效果出色。

本研究先以FER2013基本表情資料庫建立基本情緒辨識模型來驗證本研究設計的模型架構對於表情的辨識能力,在FER2013提供之測試資料集下的辨識準確率達72.01%,顯示本研究之模型對於表情辨識非常有效。

在學習情緒模型的建立上,使用四十三份學生學習時所錄製之學習影片作為訓練資料,分別以三秒鐘、一秒鐘與半秒的擷取頻率下擷取影片,以90-10的訓練與測試方式建立學習情緒預測模型,實驗結果在三秒鐘、一秒鐘、半秒擷取頻率下預測準確率可達66%、81%、91%,實驗結果說明本研究的模型對於直接辨識學習情緒是有效的,並藉由顯著圖技術視覺化出學習情緒之表情特徵,接著對本實驗室自行建立的表情資料庫進行辨識,辨識準確率可達五成以上,證明本研究的學習情緒預測模型在不同的個體差異下仍具有一定的預測能力。
摘要 i
ABSTRACT ii
目次 iv
表目次 vi
圖目次 vii
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機與目的 2
第二章 文獻探討 4
2.1 學業情緒 4
2.2 情意計算 4
2.3 情意計算方法 5
2.4 深度學習(卷積神經網路) 8
2.5 深度學習在情意計算相關研究 10
2.6 基本情緒資料庫介紹 12
第三章 研究方法 15
3.1 建立表情辨識模型之方法 15
3.1.1 資料預處理 16
3.1.2 模型架構與訓練 18
3.2 基本情緒辨識模型 26
3.2.1 基本情緒辨識結果與分析 26
3.2.2 基本情緒特徵視覺化 29
3.3 學習情緒辨識模型 31
3.3.1 學習情緒資料說明 32
3.3.2 學習情緒辨識模型之建立 32
3.3.3 學習情緒辨識模型之驗證 32
第四章 實驗結果 34
4.1 實驗環境 34
4.2 學習情緒模型實驗結果 34
4.2.1 每三秒擷取一張影像 34
4.2.2 每一秒擷取一張影像 36
4.2.3 每半秒擷取一張影像 37
4.3 學習情緒模型辨識結果與人工標記一致性之比較 41
第五章 結論與建議 43
參考文獻 44
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