本文選題：微表情識別 + 局部二值模式��； 參考：《南京郵電大學》2017年碩士論文

【摘要】：表情識別作為人機交互的一個重要領域,已經得到了幾十年的發(fā)展,在許多領域都有著廣泛的應用。近年來,人們開始研究一種特殊的表情——微表情,微表情是一種持續(xù)時間短、強度弱、反應出一個人內心真實情感的特殊表情,其在測謊、臨床診斷以及審訊等領域有著廣泛的應用。本文針對微表情進行了相關的研究,使用了靜態(tài)圖像、動態(tài)序列以及深度學習的方法進行微表情識別,主要工作內容包括:(1)對微表情圖像做預處理。本文所采用的數(shù)據(jù)庫為CASME2和SMIC微表情數(shù)據(jù),對數(shù)據(jù)庫中的微表情圖像做尺度歸一化以及灰度歸一化操作。(2)研究了基于靜態(tài)圖像的微表情識別,選出數(shù)據(jù)庫中每一個樣本的表情變化最大幀,當作該樣本的靜態(tài)微表情,提取局部二值模式(Local Binary Pattern,LBP)、局部相位量化(Local Phase Quantization,LPQ)特征并將其進行融合,實驗結果顯示,融合后的特征對微表情識別率有較大提高。(3)研究了基于動態(tài)序列的微表情識別,使用正交三維局部二值模式(Local Binary Pattern from Three Orthogonal Planes,LBP_TOP)算子提取動態(tài)序列的微表情特征,并使用局部線性嵌入算法(Locally Linear Embedding,LLE)算法對高維特征進行降維。LBP_TOP算子能夠提取微表情在時間維上的信息,相較于靜態(tài)圖像的方法,其識別率更高。(4)研究了基于深度學習的微表情識別,將微表情序列輸入3D-CNN網絡中提取微表情特征,最后使用支持向量機(Support Vector Machine,SVM)進行分類,與其他深度學習方法相比,3D-CNN能夠直接處理視頻或者圖像序列,計算簡單效率高。
[Abstract]:As an important field of human-computer interaction, facial expression recognition has been developed for decades and has been widely used in many fields.In recent years, people have begun to study a special kind of emotion-microemoji, which is a kind of special expression which is short duration, weak intensity and reflects a person's inner true emotion.Clinical diagnosis and interrogation are widely used.In this paper, we do some research on microfacial expression. We use static image, dynamic sequence and depth learning method to recognize microfacial expression. The main work includes: 1) preprocessing microfacial expression image.The database used in this paper is CASME2 and SMIC microfacial expression data. The microfacial expression recognition based on static image is studied by scale normalization and grayscale normalization operation.The maximum frame of expression change of each sample in the database is selected, which is regarded as the static micro-expression of the sample. The local binary mode of local Binary pattern is extracted and the local phase quantization (LPQs) feature of Local Phase quantification is fused. The experimental results show that,The microfacial expression recognition based on dynamic sequence is studied by using the local Binary Pattern from Three Orthogonal operator of orthogonal three-dimensional local binary mode, and the microfacial feature of dynamic sequence is extracted by using the local Binary Pattern from Three Orthogonal operator.And the locally Linear embedding algorithm is used to reduce the dimension of high dimensional feature. LBP top operator can extract the information of micro expression in time dimension, compared with the method of still image.Its recognition rate is higher. (4) the micro-expression recognition based on deep learning is studied. The micro-expression sequence is input into 3D-CNN network to extract the micro-expression feature. Finally, support vector machine (SVM) is used to classify the microfacial expression.Compared with other depth learning methods, 3D-CNN can directly process video or image sequences, and the computation is simple and efficient.
【學位授予單位】：南京郵電大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41;TP18

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本文編號：1746029