本文選題：自發(fā)微表情　切入點：雙時間尺度　出處：《西南大學》2017年碩士論文

【摘要】：人類面部表情在人們的日常生活、交流中扮演著十分重要的角色。通常,我們所指的人類面部表情被稱之為“宏表情”,其持續(xù)時間一般在0.5s~4s之間,容易被人察覺和辨別。然而,有心理學研究表明,“宏表情”在表達人類真實情感上具有一定的掩飾性,即面部“宏表情”能夠掩飾真實情感的流露,而與“宏表情”相對的“微表情”,由于其能夠表達人類試圖壓抑的情感,近年來受到了人們的廣泛關注。微表情是一種不受人控制的、簡短的面部表情,它能夠反映人試圖掩飾的情感以及人未意識到的情感體驗,因此通過“微表情”來識別人類的情感顯得更加真實、可靠。遺憾的是,由于“微表情”具有持續(xù)時間短(1/25s~1/5s),活動幅度、區(qū)域小等特點,不僅人難以識別,并且在利用模式識別等方法對微表情視頻片段進行分類識別時,很難有效的表征不同微表情所具有的特征信息;除此之外,由于自發(fā)微表情數(shù)據(jù)庫難以采集,數(shù)據(jù)量缺乏等要因素,使得訓練一個有效的微表情識別算法也變得十分艱難。針對以上問題,本文提出了一種利用雙時間尺度卷積神經(jīng)網(wǎng)絡(DTSCNN)對微表情進行識別的方法。該方法首先對微表情數(shù)據(jù)集(CASMEI、CASMEII)進行擴充處理,以此降低網(wǎng)絡訓練過程中過擬合的風險,然后利用雙通道卷積神經(jīng)網(wǎng)絡分別對微表情視頻序列在64fps和128fps兩個時間尺度進行特征提取,最后對所提取的特征采用SVM進行決策級融合分類。DTSCNN不僅解決了由于微表情數(shù)據(jù)庫樣本少、難以訓練的問題,而且在CASMEI、CASMEII數(shù)據(jù)庫上驗證的結果顯示其識別率(66.67%)比最新的、傳統(tǒng)的微表情識別算法(MDMO:55.45%、FDM:56.97%、STCLQP:56.36%)的識別率提高了10%以上。
[Abstract]:Human facial expressions play a very important role in people's daily life and communication. Usually, we refer to human facial expressions as "macro expressions". The duration of facial expressions is generally between 0.5s~4s, which is easy to be detected and distinguished. However, Psychological studies have shown that "macro expression" has a certain concealment in expressing human true emotion, that is, facial "macro expression" can conceal the expression of real emotion. "microexpressions", as opposed to "macro expressions", have attracted widespread attention in recent years for their ability to express feelings that humans are trying to suppress. Microexpressions are an uncontrolled, brief facial expression. It reflects the emotions that people try to hide and the emotional experiences they don't realize, so it's more real and reliable to identify human emotions through "microexpressions." unfortunately, Because the "microfacial expression" has the characteristics of short duration of 1 / 25 / 1 / 5 / 5 s-1, range of activity, small area, etc., it is not only difficult for people to recognize, but also in the process of classifying and recognizing microfacial video fragments by using pattern recognition and other methods. It is difficult to effectively represent the characteristic information of different microexpressions. In addition, because the spontaneous microfacial expression database is difficult to collect, the amount of data is scarce and so on. It makes it very difficult to train an effective micro-expression recognition algorithm. In this paper, a method of recognition of microfacial expression by using dual time scale convolution neural network (DTSCNN) is presented. The method firstly expands the data set of microfacial expression (CASMEI / CASMEII) to reduce the risk of over-fitting in the course of network training. Secondly, two-channel convolution neural network is used to extract the features of microfacial video sequences at 64fps and 128fps time scales, respectively. Finally, SVM is used to classify the extracted features in decision level fusion classification. DTSCNN not only solves the problem that it is difficult to be trained because of the small number of samples in the microfacial expression database, but also shows that the recognition rate is 66.67% higher than that of the latest one, which is verified on CASMEI / CASMEII database. The recognition rate of the traditional microfacial expression recognition algorithm, MDMO: 55.45 / FDM: 56.97 / STCLQP: 56.36, has increased by more than 10%.
【學位授予單位】：西南大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41;TP183

【參考文獻】

相關期刊論文 前1條

1 張軒閣;田彥濤;郭艷君;王美茜;;基于光流與LBP-TOP特征結合的微表情識別[J];吉林大學學報(信息科學版);2015年05期

，

本文編號：1660284