靜態(tài)圖像中正面人臉表情識別算法研究
發(fā)布時(shí)間：2018-12-19 21:29
【摘要】：近年來人工智能在學(xué)術(shù)界和工業(yè)界都得到了很大的發(fā)展,尤其是AlphaGo在圍棋比賽中4:1戰(zhàn)勝李世石之后,人工智能技術(shù)受到了全民的重視。人臉表情識別技術(shù)作為人機(jī)交互的重要方式,也受到了更多的重視,一些企業(yè)更是提供了判定人臉微笑與否的API給用戶使用。本課題針對目前六種基本表情識別技術(shù)并未商用的現(xiàn)狀,提出了一種簡單的在靜態(tài)圖片中識別人臉基本表情的算法框架,并在MATLAB環(huán)境下編程實(shí)現(xiàn)了文中提出的算法。本課題緊跟當(dāng)前人工智能迅速發(fā)展的步伐,對靜態(tài)圖片中六種基本人臉表情的自動識別技術(shù)進(jìn)行了深入地研究。為了驗(yàn)證本文算法的正確性,本文提出的算法在CK+與JAFFE數(shù)據(jù)庫上進(jìn)行了應(yīng)用。同時(shí),本文提出算法獲得的識別率與一些文獻(xiàn)中獲得的識別率進(jìn)行了比較,進(jìn)一步說明了該算法的正確性和有效性。本文的主要研究內(nèi)容和創(chuàng)新研究如下:(1)本文研究了靜態(tài)圖片中六種基本表情與其中性臉的相關(guān)性,將卡爾·皮爾森相關(guān)系數(shù)應(yīng)用到了定義人臉表情活躍區(qū)域的研究工作之上。本文首先對比了各個(gè)表情與其中性臉的靜態(tài)圖像的相關(guān)性,然后根據(jù)各種表情與其中性臉的相關(guān)性數(shù)值的大小,確定了人臉表情的活躍區(qū)域。(2)本文在人臉表情識別研究上首次提出并應(yīng)用了活躍區(qū)域歸一化方法。本文首先歸一化了人臉活躍區(qū)域,并在人臉的活躍區(qū)域提取了 LBP與HOG特征。其中,為了更精確地定位人臉的活躍區(qū)域,一種在人臉上定位多個(gè)精確關(guān)鍵點(diǎn)的人臉對齊算法被應(yīng)用到了本文中。(3)LBP與HOG特征在歸一化之后的活躍區(qū)域中被提取出來,并且在該文章的實(shí)驗(yàn)中這兩種特征被較好地融合在了一起。實(shí)驗(yàn)證明,兩種特征融合之后的識別率較之單個(gè)特征效果更好。本文首次將伽瑪校正方法應(yīng)用到了 LBP特征之上,該方法在很大程度上提高了人臉表情的識別率。(4)本文定義了σ參數(shù),研究者可以通過找到該參數(shù)的最大值來找到合適的伽瑪校正值。本文設(shè)計(jì)的實(shí)驗(yàn)證明了該參數(shù)很大程度上縮減了找到合適伽瑪校正值的工作量。(5)在本文中,一種基于手工提取特征識別六種基本人臉表情的算法框架被提出,并且該算法框架被應(yīng)用到了 CK+和JAFFE數(shù)據(jù)庫之上。本文提出的算法在CK+數(shù)據(jù)庫上取得了目前已知最好的識別率,同時(shí)其在JAFFE上也取得了很有競爭力的識別結(jié)果。
[Abstract]:In recent years, artificial intelligence has been greatly developed in both academia and industry, especially after AlphaGo defeated Li Shishi at 4:1 in the go game, artificial intelligence technology has been attached great importance to by all people. As an important way of human-computer interaction, facial expression recognition technology has been paid more attention to, and some enterprises provide users with API to judge whether people smile or not. In view of the fact that six basic facial expression recognition techniques are not commercially available at present, this paper proposes a simple algorithm framework for facial expression recognition in static images, and implements the proposed algorithm under the MATLAB environment. Following the rapid development of artificial intelligence, the automatic recognition technology of six basic facial expressions in static images is studied in this paper. In order to verify the correctness of the proposed algorithm, the proposed algorithm is applied to CK and JAFFE databases. At the same time, the recognition rate obtained by the proposed algorithm is compared with that obtained in some literatures, which further demonstrates the correctness and effectiveness of the algorithm. The main contents and innovations of this paper are as follows: (1) this paper studies the correlation between six basic expressions and their neutral faces in static pictures. Karl Pearson correlation coefficient is applied to the research work of defining the active region of facial expression. This paper first compares the correlation between each expression and the static image of its neutral face, and then according to the magnitude of the correlation between each expression and its neutral face, The active region of facial expression is determined. (2) in the research of facial expression recognition, the method of active region normalization is proposed and applied for the first time. In this paper, the active region of human face is normalized, and the LBP and HOG features are extracted from the active region of the face. In order to locate the active region of human face more accurately, a human face alignment algorithm is applied in this paper. (3) LBP and HOG features are extracted from the normalized active region. And in the experiment of this paper, the two features are well fused together. Experiments show that the recognition rate of the two features is better than that of a single feature. In this paper, the gamma-ray correction method is applied to LBP features for the first time. This method improves the recognition rate of facial expressions to a great extent. (4) the 蟽 parameters are defined in this paper. Researchers can find the appropriate gamma correction value by finding the maximum value of the parameter. The experiments designed in this paper show that this parameter greatly reduces the workload of finding the appropriate gamma correction value. (5) in this paper, an algorithm framework based on manual feature extraction for recognition of six basic facial expressions is proposed. And the algorithm framework is applied to CK and JAFFE database. The algorithm proposed in this paper has obtained the best recognition rate in the CK database at present, and it has also obtained the competitive recognition result on the JAFFE at the same time.
【學(xué)位授予單位】：山東大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41;TP18
，
本文編號：2387492
[Abstract]:In recent years, artificial intelligence has been greatly developed in both academia and industry, especially after AlphaGo defeated Li Shishi at 4:1 in the go game, artificial intelligence technology has been attached great importance to by all people. As an important way of human-computer interaction, facial expression recognition technology has been paid more attention to, and some enterprises provide users with API to judge whether people smile or not. In view of the fact that six basic facial expression recognition techniques are not commercially available at present, this paper proposes a simple algorithm framework for facial expression recognition in static images, and implements the proposed algorithm under the MATLAB environment. Following the rapid development of artificial intelligence, the automatic recognition technology of six basic facial expressions in static images is studied in this paper. In order to verify the correctness of the proposed algorithm, the proposed algorithm is applied to CK and JAFFE databases. At the same time, the recognition rate obtained by the proposed algorithm is compared with that obtained in some literatures, which further demonstrates the correctness and effectiveness of the algorithm. The main contents and innovations of this paper are as follows: (1) this paper studies the correlation between six basic expressions and their neutral faces in static pictures. Karl Pearson correlation coefficient is applied to the research work of defining the active region of facial expression. This paper first compares the correlation between each expression and the static image of its neutral face, and then according to the magnitude of the correlation between each expression and its neutral face, The active region of facial expression is determined. (2) in the research of facial expression recognition, the method of active region normalization is proposed and applied for the first time. In this paper, the active region of human face is normalized, and the LBP and HOG features are extracted from the active region of the face. In order to locate the active region of human face more accurately, a human face alignment algorithm is applied in this paper. (3) LBP and HOG features are extracted from the normalized active region. And in the experiment of this paper, the two features are well fused together. Experiments show that the recognition rate of the two features is better than that of a single feature. In this paper, the gamma-ray correction method is applied to LBP features for the first time. This method improves the recognition rate of facial expressions to a great extent. (4) the 蟽 parameters are defined in this paper. Researchers can find the appropriate gamma correction value by finding the maximum value of the parameter. The experiments designed in this paper show that this parameter greatly reduces the workload of finding the appropriate gamma correction value. (5) in this paper, an algorithm framework based on manual feature extraction for recognition of six basic facial expressions is proposed. And the algorithm framework is applied to CK and JAFFE database. The algorithm proposed in this paper has obtained the best recognition rate in the CK database at present, and it has also obtained the competitive recognition result on the JAFFE at the same time.
【學(xué)位授予單位】：山東大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41;TP18
，
本文編號：2387492
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