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  本文選題：三維人臉表情識別 + 特征提取�。� 參考：《北京交通大學》2016年博士論文

【摘要】：表情是人類情感理解和分析的主要載體之一。人臉表情識別的目的是讓計算機學習人類的情感表達,使其能像人類一樣具有識別、理解和表達情感的能力。隨著人們對計算機智能性要求的日益增強,該課題已受到國內外研究機構的廣泛關注。傳統基于二維圖像的人臉表情識別已取得較好的識別結果，，但仍存在一些問題沒有解決,如光照及姿態(tài)變化等,而這是由二維圖像的固有屬性決定的,因此,基于二維圖像的人臉表情識別很難突破該類問題。三維人臉表情是對表情的三維形狀的表達,其獲取不受光照等外部環(huán)境變化的影響。因此,三維人臉表情能夠有效避免這些外部因素的影響,從而獲得較好的識別結果。本文在對三維人臉表情深入分析的基礎上,針對特征提取算法進行研究。主要的研究內容和創(chuàng)新點包括以下四個方面：1.引入夾角特征,并提出其與距離特征融合的人臉表情特征描述形式基于FACS (Facial Action Coding System)對不同人臉表情定義的對比,本文首先提出夾角特征用于表征表情變化引起的人臉形變信息。而傳統距離特征可用于描述人臉上發(fā)生的相對位移,這兩組特征分別對人臉表情變化的不同屬性進行描述,使得它們的特征分布的相關性較小。因此,本文進一步提出采用這兩組特征融合的方法獲取更多的人臉表情變化信息量,從而實現對人臉表情更精準的描述。實驗表明,本文提出的夾角特征對人臉表情的表征具有一定的適用性,且它與距離特征的融合能夠有效地改善三維人臉表情的識別結果。2.提出了基于PCA相對熵的特征鑒別力判斷準則根據貝葉斯理論分析可得,特征的鑒別力依賴其異構的條件概率分布,相對熵可用于評估兩組概率分布之間的差異。本文基于算術平均法改進了相對熵使其具有對稱性,以滿足度量要求。同時,融入PCA算法,使得PCA相對熵具有更好的普適性。該準則計算復雜度小,可用于有效地選取鑒別力強的表情特征。實驗中對三維人臉表情提取多組特征,并采用多種分類器進行表情識別。結果表明,三維人臉表情的識別結果與其所采用特征的PCA相對熵的相對變化趨勢保持一致,從而驗證了該準則的有效性。3.提出了基于平均中性臉生成人臉表情深度差分圖的算法本文首先提出采用三維柵格化的方法將三維人臉的存儲形式轉換為方格結構,并統計獲得三維人臉的平均中性臉,進而去除原三維人臉表情的中性臉成分,并生成其對應的深度圖。由于該類人臉表情深度圖去除了中性臉成分,僅由表情的殘留成分表征人臉表情的變化,使得所獲得的人臉表情特征具有更好的表情可分性和個體無關性。實驗表明,基于平均中性臉生成的人臉表情深度差分圖保留了人臉表情變化的主要信息,并有效地保持了人臉表情變化的相對強度及其主要集中于人臉局部區(qū)域的特性。該類深度圖能夠有效地表征人臉表情變化的本質特性,提高其識別結果,這進一步驗證了利用深度圖分析三維人臉表情的有效性。4.提出了一種有效的三維人臉表情特征：IreEnLBP本文首先提出“類圖像方格結構”,并基于該結構實現三維人臉表情的預處理,使得傳統的特征可直接用于表征三維人臉的表情變化。進而提出了基于人臉特征分布的“不均勻子塊劃分法”和“熵加權算法”,并應用于LBP特征,最終提出一種有效的三維人臉表情特征：IreEnLBP.該特征不僅具有LBP特征的局部描述特性,而且“不均勻子塊劃分法”依照人臉主要器官的分布情況劃分子塊,以保證人臉局部器官結構的完整性,使得不同表情對應劃分出不同的人臉子塊,進而提高所提取特征的類間可分性。同時,基于每一子塊的熵值對局部特征賦予權重,使得所獲得的IreEnLBP特征體現出不同局部區(qū)域對表情變化的影響,增強其對不同表情描述的獨特性。實驗表明,IreEnLBP特征能夠有效地提高三維人臉表情識別結果。
[Abstract]:Facial expression is one of the main carriers of human emotion understanding and analysis. The purpose of facial expression recognition is to let the computer learn the emotional expression of human beings and make it capable of recognizing, understanding and expressing emotions like human beings. With the increasing demand for computer intelligence, the subject has been widely used by research institutions both at home and abroad. The traditional two-dimensional image based facial expression recognition has obtained better recognition results, but there are still some problems that are not solved, such as illumination and posture change, which are determined by the inherent properties of two-dimensional images. Therefore, facial expression recognition based on two-dimensional images is difficult to break through this kind of problem. The expression of three-dimensional shape is not affected by the changes of the external environment such as illumination. Therefore, the three-dimensional facial expression can effectively avoid the influence of these external factors and obtain better recognition results. Based on the in-depth analysis of the three-dimensional facial expression, this paper studies the feature extraction algorithm. The main research content and the main research content are in this paper. The innovation points include the following four aspects: 1. introducing the feature of the angle, and putting forward the representation of facial expression with the fusion of distance features based on the comparison of different facial expressions based on FACS (Facial Action Coding System). In this paper, the angle feature is first proposed to represent the human face change information caused by the change of the surface condition. The features can be used to describe the relative displacement on the face. These two sets of features respectively describe the different attributes of facial expression changes, making the correlation of their features smaller. Therefore, this paper further proposes to use these two sets of feature fusion methods to obtain more facial expression change information, thus realizing the face table. The experiment shows that the angle feature proposed in this paper has certain applicability to the representation of facial expression, and it can effectively improve the recognition results of 3D facial expression with the fusion of distance features.2. proposed the characteristic discriminability criterion based on the relative entropy of PCA based on Bias theory. The discriminability depends on its heterogeneous conditional probability distribution, and the relative entropy can be used to evaluate the differences between the two groups of probability distributions. Based on the arithmetic mean method, the relative entropy is improved to make it symmetric to meet the measurement requirements. At the same time, the PCA algorithm is incorporated into the PCA relative entropy to have a better universality. The results show that the recognition result of 3D facial expression is consistent with the relative change trend of the relative entropy of PCA, which verifies the validity of the criterion.3.. In this paper, an algorithm for generating facial expression depth differential graph with average neutral face is proposed in this paper. Firstly, a three-dimensional grid method is proposed to convert the storage form of 3D face into square structure, and the average neutral face of the 3D face is obtained, and then the neutral face component of the original 3D face is removed and its corresponding depth map is generated. The depth map of the facial expression removes the neutral face component. It only characterizes the changes of the facial expression by the residual components of the expression, making the facial expression features better expressive and individual independent. The experiment shows that the facial expression depth difference graph based on the average neutral face preserves the main letter of the facial expression change. It also effectively maintains the relative intensity of facial expression changes and the characteristics that mainly focus on the local area of the face. This kind of depth map can effectively characterize the essential characteristics of facial expression changes and improve the recognition results. This further verifies that the effectiveness of the 3D facial expression analysis by using the depth map is an effective.4.. Three dimensional facial expression features: IreEnLBP first proposed "class image square structure", and based on this structure, the three-dimensional facial expression was preprocessed. The traditional features can be used to represent the facial expression changes directly. Then the "uneven subdivision method" and "entropy weighting algorithm" based on the distribution of facial features were proposed. "And applied to the features of LBP, an effective three-dimensional facial expression feature is proposed. IreEnLBP. features not only the local characterization of LBP features, but the" uneven subdivision method "divides the sub blocks according to the distribution of the main organs of the face in order to ensure the integrity of the organ structure of the human face, and make the different expressions corresponding to them. Different human face blocks are divided, and then the separability between the extracted features is improved. At the same time, based on the entropy value of each sub block, the local feature is given weight, which makes the IreEnLBP features reflect the influence of different local regions on the expression change and enhance its uniqueness to different expressions. The experiment shows that the IreEnLBP features can be characterized. The results of 3D facial expression recognition are effectively improved.

【學位授予單位】：北京交通大學
【學位級別】：博士
【學位授予年份】：2016
【分類號】：TP391.41

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本文編號：1887372