本文選題：微表情識(shí)別 + 局部二值模式　； 參考：《南京郵電大學(xué)》2017年碩士論文

【摘要】：表情識(shí)別作為人機(jī)交互的一個(gè)重要領(lǐng)域,已經(jīng)得到了幾十年的發(fā)展,在許多領(lǐng)域都有著廣泛的應(yīng)用。近年來(lái),人們開(kāi)始研究一種特殊的表情——微表情,微表情是一種持續(xù)時(shí)間短、強(qiáng)度弱、反應(yīng)出一個(gè)人內(nèi)心真實(shí)情感的特殊表情,其在測(cè)謊、臨床診斷以及審訊等領(lǐng)域有著廣泛的應(yīng)用。本文針對(duì)微表情進(jìn)行了相關(guān)的研究,使用了靜態(tài)圖像、動(dòng)態(tài)序列以及深度學(xué)習(xí)的方法進(jìn)行微表情識(shí)別,主要工作內(nèi)容包括:(1)對(duì)微表情圖像做預(yù)處理。本文所采用的數(shù)據(jù)庫(kù)為CASME2和SMIC微表情數(shù)據(jù),對(duì)數(shù)據(jù)庫(kù)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(shí)別,選出數(shù)據(jù)庫(kù)中每一個(gè)樣本的表情變化最大幀,當(dāng)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(jìn)行融合,實(shí)驗(yàn)結(jié)果顯示,融合后的特征對(duì)微表情識(shí)別率有較大提高。(3)研究了基于動(dòng)態(tài)序列的微表情識(shí)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(dòng)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(duì)高維特征進(jìn)行降維。LBP_TOP算子能夠提取微表情在時(shí)間維上的信息,相較于靜態(tài)圖像的方法,其識(shí)別率更高。(4)研究了基于深度學(xué)習(xí)的微表情識(shí)別,將微表情序列輸入3D-CNN網(wǎng)絡(luò)中提取微表情特征,最后使用支持向量機(jī)(Support Vector Machine,SVM)進(jìn)行分類(lèi),與其他深度學(xué)習(xí)方法相比,3D-CNN能夠直接處理視頻或者圖像序列,計(jì)算簡(jiǎn)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(xué)位授予單位】：南京郵電大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TP391.41;TP18

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本文編號(hào)：1746029