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研究生:葉冠捷
研究生(外文):YEH, KUAN-CHIEH
論文名稱:利用數學形態學與基因演算法於臉部表情之偵測
論文名稱(外文):Detection of Facial Expressions Using Mathematical Morphology and Genetic Algorithms
指導教授:陳重臣陳重臣引用關係
指導教授(外文):CHEN, JONG-CHEN
口試委員:許中川呂德財陳重臣
口試委員(外文):HSU, CHUNG-CHAINLu, TE-TSAICHEN, JONG-CHEN
口試日期:2018-06-14
學位類別:碩士
校院名稱:國立雲林科技大學
系所名稱:資訊管理系
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:2018
畢業學年度:106
語文別:中文
論文頁數:52
中文關鍵詞:人臉表情辨識數學形態學基因演算法臉部關鍵點偵測
外文關鍵詞:Face expression recognitionMathematical morphologyGenetic algorithmFacial landmarks
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本文提出一種演化學習系統,可以自動產生形態學之程式,並搭配基因演算法不斷的增強形態學的變化,再透過臉部目標特徵,期望能透過此演化學習系統發展出可以區分臉部特徵表情的分類器。因此,須先進行影像的前置處理,找出臉部的特徵(眼、眉毛、嘴巴、鼻子),並以此為基底搭配Jaffe資料集中的7種表情類型進行演化學習系統的訓練,演化學習的第一階段是擴展,產生形態學序列,以增加族群多樣性。第二階段是合成,將產生的形態學序列進行結合,以產生不同的分類器。第三階段是選擇,將產生的分類器進行分數的計算,並選出優異的族群。第四階段是複製,複製優異的族群。第五階段為突變,將複製的族群進行突變,以發展出多元化的族群。最後,透過演化學習系統訓練出的分類器區分不同臉部特徵表情時,可以有76%的辨識率,雖然準確度並不是很高,倘若加以改良此學習系統,相信能提升辨識率已應用在生活之中。
Based on an evolutionary learning system, call MORPH which is used to recognize alphabets, our goal is to develop an innovative method of facial expression recognition. We wish to develop a filter that can distinguish facial features and expressions through this evolutionary learning system. Therefore, we have to do the preprocessing, find out the facial feature (eyes, eyebrows, mouths and noses) based on this and collocate with seven different types of expressions from Jaffe dataset to do the evolutionary learning system. The first step is “Expand”, generate the morphology sequences to increase population and diversity. The second step is “Compose”, combine the morphology sequences to generate different filters. The third step is “Select”. Calculate the score from the filters and select the outstanding population. The forth step is “Copy”, copy the outstanding population. The fifth step is “Mutation”, mutate the copied population to develop diverse population. Finally, when we distinguish different expression features through evolutionary learning system, the recognition rate could be 76 percent, Although the accuracy is not very high, if we improve this learning system, I believe it can improve the recognition rate and apply it to life.
摘要 i
Abstract ii
目錄 iii
表目錄 v
圖目錄 vi
壹、 緒論 1
1.1研究背景與動機 1
1.2研究目的 1
1.3 論文架構 2
貳、文獻探討 3
2.1 臉部表情辨識 3
2.2 邊緣偵測 4
2.3數學形態學 6
2.4 基因演算法 8
參、 研究架構與方法 12
3.1資料集 13
3.2影像前處理 13
3.2.1臉部偵測 14
3.2.2 臉部關鍵點偵測 16
3.2.3 二值化 17
3.2.4 邊緣偵測 17
3.3 演化學習 18
3.3.1 初始參數 19
3.3.2 擴展 20
3.3.3合成 22
3.3.4選擇 25
3.3.5複製 26
3.3.6突變 26
3.4 臉部辨識情形 27
肆、 實驗過程與結果 28
4.1影像前處理 28
4.2 演化學習結果 30
4.2.1 演化情況 30
4.2.2 演化分配情形 31
4.3 對訓練集進行雜訊的壓力實驗 34
4.4 相同表情不同呈現之分類實驗 36
伍、 結論與未來展望 39
5.1研究結論 39
5.2 未來展望 39
參考文獻 40
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