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結合動差運算子與區域紋理特徵之臉部表情辨識 = Combined Mom...

國立高雄大學電機工程學系碩士班

結合動差運算子與區域紋理特徵之臉部表情辨識 = Combined Moment Operator and Local Texture-based Features in Facial Expression Recognition

Record Type:	Language materials, printed : monographic
Paralel Title:	Combined Moment Operator and Local Texture-based Features in Facial Expression Recognition
Author:	邱建璋,
Secondary Intellectual Responsibility:	國立高雄大學
Place of Publication:	[高雄市]
Published:	撰者;
Year of Publication:	2015[民104]
Description:	[13], 53面圖，表 : 30公分;
Subject:	表情辨識
Subject:	Facial expression recognition
Online resource:	http://handle.ncl.edu.tw/11296/ndltd/77751696771093571110
Notes:	參考書目：面50-53
Notes:	104年3月25日公開
Summary:	隨著日益增加的應用，自動化的臉部表情辨識已成為相當熱門的研究主題。臉部表情辨識主要透過人臉影像進行臉部特徵擷取，再將取出的特徵經由分類器進行表情的分類。本論文提出一個以改良式區域方向圖樣與澤尼克動差為基準之特徵擷取方法，對臉部表情影像擷取具有辨識能力的特徵，再將取得的臉部特徵以支持向量機進行分類。實驗結果證實，我們提出的方法在著名的Cohn-Kanade、JAFFE以及TFEID表情資料庫中，有更好的辨識效能。 Automatic facial expression recognition has been a subject of investigation in the last years due to the great number of potential applications. In this paper, we proposed a facial expression representation approach which combines improved Local Directional Pattern (iLDP) and Zernike moments. To extract the discriminative facial features from facial images, and then these features are classified by the support vector machine. Experimental results are provided to illustrate the proposed approach is an effective method on the Cohn-Kanade, JAFFE, and TEFID databases.

結合動差運算子與區域紋理特徵之臉部表情辨識 = Combined Moment Operator and Local Texture-based Features in Facial Expression Recognition
邱, 建璋

結合動差運算子與區域紋理特徵之臉部表情辨識 = Combined Moment Operator and Local Texture-based Features in Facial Expression Recognition / 邱建璋撰 - [高雄市] : 撰者, 2015[民104]. - [13], 53面 ; 圖，表 ; 30公分.
參考書目：面50-53104年3月25日公開.
表情辨識Facial expression recognition

結合動差運算子與區域紋理特徵之臉部表情辨識 = Combined Moment Operator and Local Texture-based Features in Facial Expression Recognition
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330 $a 隨著日益增加的應用，自動化的臉部表情辨識已成為相當熱門的研究主題。臉部表情辨識主要透過人臉影像進行臉部特徵擷取，再將取出的特徵經由分類器進行表情的分類。本論文提出一個以改良式區域方向圖樣與澤尼克動差為基準之特徵擷取方法，對臉部表情影像擷取具有辨識能力的特徵，再將取得的臉部特徵以支持向量機進行分類。實驗結果證實，我們提出的方法在著名的Cohn-Kanade、JAFFE以及TFEID表情資料庫中，有更好的辨識效能。 Automatic facial expression recognition has been a subject of investigation in the last years due to the great number of potential applications. In this paper, we proposed a facial expression representation approach which combines improved Local Directional Pattern (iLDP) and Zernike moments. To extract the discriminative facial features from facial images, and then these features are classified by the support vector machine. Experimental results are provided to illustrate the proposed approach is an effective method on the Cohn-Kanade, JAFFE, and TEFID databases.
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