臺灣大學: 電信工程學研究所丁建均劉俊佐Liu, Jun-ZuoJun-ZuoLiu2013-03-272018-07-052013-03-272018-07-052012http://ntur.lib.ntu.edu.tw//handle/246246/252553基於可接觸資料的增加與計算技術的快速發展，過去十年，機器學習已因為人類生活中大量的自動化需求而吸引了許多注意力。現今在物件識別、機器人學、人工智慧、電腦視覺、甚至是經濟學等科學領域當中，機器學習已成為從資料當中抽取與探索重要資訊不可或缺的角色。 另一方面，在過幾十年，人臉偵測與辨識等人臉相關的主題已漸漸成為物件識別與電腦視覺中的重要研究領域。其原因來自於自動化識別與監視系統的需求、對於人類視覺系統在人臉感知上的興趣、與人機互動介面的設計開發等。 在本篇論文當中，我們專注於使用機器學習技術來測定人類的表情。我們提出了一個人類表情辨識的架構，其包含了特徵抽取、抗雜訊機制、降低維度、與表情測定等四個步驟。我們改善了傳統的區域二質化樣本特徵抽取方法 (local binary pattern, LBP) 並結合另一種近年發展出的區域相位量化特徵抽取方法 (local phase quantization, LPQ) 作為人臉表情的特徵表示式。為了讓抽取出的人臉表情特徵更具代表性並消除對於表情辨識不重要的特徵，我們特別提出了一種抗雜訊機制。這個機制可讓我們更妥善地利用擷取出的表情特徵使得之後的降維及辨識工作更具效果。不同於以往的降維方法，我們特別根據人臉表情的特性設計了針對表情辨識而做的降維方法。 最後根據降維完後的表情特徵，我們使用常見的支持向量機和某些個最近鄰居分類器 (support vector machine, SVM and K-nearest neighbor, KNN) 來判斷可能的表情。 實驗結果顯示，在普遍使用的JAFFE資料庫中，我們提出的架構和提出的演算法跟現有的其他方法比較能達到較好的辨識率。Based on the increasing of accessible data and the fast development of the computational technology, machine learning attracted lots of attention in the last ten year because of the great demand of automation in human life. Now in the disciplines of pattern recognition, robotics, artificial intelligences, computer vision, and even economics, machine learning has been an indispensible part to extract and discover the valuable information from data. On the other hand, human face related topics such as face detection and recognition became important research fields in pattern recognition and computer vision during the last few decades. This is due to the needs of automatic recognition and surveillance system, the interest in the human visual system on human face perception, and the design of human-computer interface, etc. In this thesis, we focus on using machine learning techniques for facial expression recognition. A facial expression recognition framework is proposed, which includes four steps: feature extraction, denoising mechanism, dimensionality reduction, and facial expression determination. The widely-used local binary pattern feature (LBP) is modified and combined with a new feature extraction method, local phase quantization (LPQ) to represent the facial expression. Since the extracted features are noisy and contain unrelated information for expression recognition task, a denoising mechanism is proposed. Due to the denoising mechanism, the denoised features are more representative for facial expression. Different from the existing dimensionality reduction algorithms, an expression-specific dimensionality reduction algorithm is proposed based on the special properties of facial expression. Finally, the reduced features with more meaning for facial expression are fed into the widely-used Support Vector Machine (SVM) and K-nearest neighbor classifier. From the experimental results, the proposed framework and algorithms achieve the highest recognition rate against the existing methods based on the JAFFE database.3462511 bytesapplication/pdfen-US機器學習特徵抽取降維流形學習人臉表情辨識Machine learningfeature extractiondimensionality reductionmanifold learningfacial expression recognitionthesishttp://ntur.lib.ntu.edu.tw/bitstream/246246/252553/1/ntu-101-R99942103-1.pdf