本文選題：人臉表情識別 + 深度學(xué)習　； 參考：《南京郵電大學(xué)》2017年碩士論文

【摘要】：人臉表情能夠表達人類的情緒、意圖等,人臉表情識別作為情感智能系統(tǒng)的關(guān)鍵技術(shù),是實現(xiàn)人機交互的重要基礎(chǔ)。傳統(tǒng)的表情識別中依靠人工精心設(shè)計的特征提取算法,不僅復(fù)雜,還會一定程度上丟失原有的表情特征信息。近年來,深度學(xué)習作為以純數(shù)據(jù)為驅(qū)動的特征學(xué)習算法,能夠自主地學(xué)習到樣本的更加本質(zhì)的特征,因此,本文將深度學(xué)習引入人臉表情識別任務(wù)中,探討及研究深度學(xué)習在表情識別中的應(yīng)用。本文的主要研究工作及成果總結(jié)如下:(1)擴增了人臉表情庫。人臉表情庫是表情識別的基本條件,本文對現(xiàn)有的表情庫進行人臉檢測、歸一化等預(yù)處理,并采用數(shù)據(jù)集擴增策略擴增了人臉表情庫。(2)研究了一種基于深度置信網(wǎng)絡(luò)(Deep Belief Network,DBN)的人臉表情識別方法。通過預(yù)訓(xùn)練和微調(diào)階段調(diào)整優(yōu)化DBN模型參數(shù),最頂層BP網(wǎng)絡(luò)輸出表情分類結(jié)果,在CK+數(shù)據(jù)庫上取得了91.16%的識別率。(3)研究了一種基于卷積神經(jīng)網(wǎng)絡(luò)(Convolutional Neural Network,CNN)的人臉表情識別方法。人臉表情的變化往往是細微的,CNN可以捕捉到圖像的局部特征,組合低層特征構(gòu)成更加抽象的高層特征,從而更適合于二維表情圖像的分類。相比DBN,基于CNN的人臉表情識別方法取得的識別率提高了5.02%。(4)研究了一種基于NIN(Network in Network)的人臉表情識別方法。先對低層特征局部進行更好地抽象有利于提升高層特征的表征能力,NIN的卷積層具有較強的非線性特征提取能力,因而有利于復(fù)雜的人臉表情圖像的非線性特征的抽象和表達。相比CNN,基于NIN的人臉表情識別方法取得的識別率進一步提高了2.79%。(5)實現(xiàn)了一個人臉表情識別演示系統(tǒng)。該系統(tǒng)主要分為兩個功能,一是人臉表情自動識別,將人臉表情分為七類;二是卷積層可視化,可以直觀地觀察到每個卷積層卷積運算后輸出的特征圖。
[Abstract]:Facial expression can express human emotion, intention and so on. As a key technology of emotional intelligence system, facial expression recognition is an important foundation of human-computer interaction. Traditional facial expression recognition based on artificial carefully designed feature extraction algorithm is not only complex, but also lose the original facial feature information to some extent. In recent years, as a feature learning algorithm driven by pure data, depth learning is able to learn more essential features of samples independently. Therefore, depth learning is introduced into facial expression recognition task in this paper. To explore and study the application of depth learning in facial expression recognition. The main work and results of this paper are summarized as follows: 1) the facial expression database is expanded. Facial expression database is the basic condition of facial expression recognition. An expression recognition method based on Deep Belief Network (DBN) is proposed. By adjusting the parameters of the DBN model in pre-training and fine-tuning stage, the top-level BP network outputs the facial expression classification results. The recognition rate of 91.16% is obtained on CK database.) A new facial expression recognition method based on Convolutional Neural Network (CNN) based on convolutional neural network is studied. The change of facial expression is usually subtle CNN can capture the local features of the image and combine the low-level features to form a more abstract high-level feature which is more suitable for the classification of two-dimensional facial expression images. Compared with DBN, the recognition rate of facial expression recognition method based on CNN is 5.022.The paper studies a face expression recognition method based on NIN(Network in Network. Firstly, the local abstraction of lower level features is helpful to enhance the representation ability of high-level features. The convolution layer of NIN has a strong ability to extract nonlinear features, which is conducive to the abstraction and expression of nonlinear features in complex facial expression images. Compared with the CNN-based facial expression recognition method, the recognition rate obtained by the NIN method is further improved by 2.79. 5) A human facial expression recognition demonstration system is implemented. The system is mainly divided into two functions, one is automatic facial expression recognition, facial expression is divided into seven categories, the other is convolution layer visualization, can directly observe each convolution layer after convolution output features.
【學(xué)位授予單位】：南京郵電大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41;TP181

【參考文獻】

相關(guān)期刊論文 前6條

1 盧官明;何嘉利;閆靜杰;李海波;;一種用于人臉表情識別的卷積神經(jīng)網(wǎng)絡(luò)[J];南京郵電大學(xué)學(xué)報(自然科學(xué)版);2016年01期

2 盧官明;石婉婉;李旭;李曉南;陳夢瑩;劉莉;;基于LBP特征和稀疏表示的新生兒疼痛表情識別[J];南京郵電大學(xué)學(xué)報(自然科學(xué)版);2015年01期

3 盧官明;左加闊;;基于不相關(guān)局部敏感鑒別分析的新生兒疼痛表情識別[J];南京郵電大學(xué)學(xué)報(自然科學(xué)版);2013年06期

4 余凱;賈磊;陳雨強;徐偉;;深度學(xué)習的昨天、今天和明天[J];計算機研究與發(fā)展;2013年09期

5 薛雨麗;毛峽;張帆;;BHU人臉表情數(shù)據(jù)庫的設(shè)計與實現(xiàn)[J];北京航空航天大學(xué)學(xué)報;2007年02期

6 梁路宏 ,艾海舟 ,徐光yP ,張鈸;人臉檢測研究綜述[J];計算機學(xué)報;2002年05期

相關(guān)碩士學(xué)位論文 前1條

1 史于心;基于混合特征的面部表情識別算法研究[D];吉林大學(xué);2007年

，

本文編號：1901243