本文選題：人臉表情識別 + 協(xié)作表示��； 參考：《山東大學(xué)》2017年碩士論文

【摘要】：隨著人機交互、情感分析及情感計算等技術(shù)的深入研究,人臉表情識別技術(shù)得到了飛速的發(fā)展。面部表情是非語言交流的一種重要表現(xiàn)形式,是人們理解情感的重要途徑。人臉表情識別表現(xiàn)出重要的理論研究價值和實際應(yīng)用價值,已逐漸成為人工智能與計算機視覺領(lǐng)域熱門的研究方向。近年來,基于壓縮感知的稀疏表示開始廣泛地應(yīng)用于目標跟蹤、人臉識別等圖像處理領(lǐng)域。然而,稀疏表示分類算法忽略了類別間的協(xié)作表示對分類的影響,協(xié)作表示分類算法既充分發(fā)揮了稀疏性對分類的優(yōu)勢,又兼顧了類別間的協(xié)作表示對分類的提升。但是,目前并沒有有效的協(xié)作表示分類算法應(yīng)用于人臉表情識別系統(tǒng)。針對以上問題,基于協(xié)作表示分類模型,本論文提出了基于協(xié)作表示參數(shù)估計的人臉表情識別算法,主要完成了以下三方面的研究:(1)基于l2范數(shù)的協(xié)作表示分類模型,本論文對協(xié)作表示保真項進行范數(shù)近似估計,并引入正則化因子對其改進。(2)在協(xié)作表示分類模型的基礎(chǔ)上,本論文提出了一種基于最大似然估計的正則化加權(quán)協(xié)作表示分類算法及其模型。該算法通過對協(xié)作表示保真項作加權(quán)迭代分析,實現(xiàn)了人臉表情像素的自適應(yīng)權(quán)值分配,降低了邊界像素的識別干擾;通過對協(xié)作表示保真項作最大似然估計,使協(xié)作殘差最小化,有效提高了人臉表情識別系統(tǒng)的有效性。(3)從貝葉斯估計的角度,本論文提出了基于最大后驗估計的正則化加權(quán)協(xié)作表示分類算法及其模型。該算法通過對協(xié)作表示保真項作最大后驗估計,引入先驗因子,實現(xiàn)了對人臉表情識別系統(tǒng)的多角度、多層次的評估;通過對先驗因子的分布參數(shù)作牛頓迭代估計,有效簡化了人臉表情識別系統(tǒng)的算法復(fù)雜度�；谧畲笏迫还烙嫷恼齽t化加權(quán)協(xié)作表示分類算法及其模型提高人臉表情識別的準確性和自適應(yīng)性,而基于最大后驗估計的正則化加權(quán)協(xié)作表示分類算法及其模型實現(xiàn)對人臉表情識別系統(tǒng)的多角度、多層次的評估。以上研究,為人臉表情識別的協(xié)作模型提供了一種行之有效的機制,同時提供了一種高精度、高魯棒性的算法,為人臉表情識別系統(tǒng)的實際應(yīng)用打下了堅實的基礎(chǔ)。
[Abstract]:With the in-depth study of human-computer interaction, emotional analysis and emotional computing, facial expression recognition technology has developed rapidly. Facial expression is an important form of non verbal communication and an important way for people to understand emotion. Facial expression recognition shows important theoretical research value and practical application value. In recent years, the sparse representation based on compressed sensing has been widely used in target tracking, face recognition and other image processing fields. However, the sparse representation classification algorithm ignores the effect of the cooperative representation among categories, and the cooperative representation classification algorithm is not only sufficient. The superiority of the sparsity to the classification is brought into play, and the collaboration among categories is taken into account to improve the classification. However, there is no effective cooperative representation classification algorithm applied to facial expression recognition system at present. The following three aspects are completed mainly: (1) a cooperative representation model based on L2 norm. This paper estimates the norm of the fidelity item by cooperative representation and introduces the regularization factor to improve it. (2) on the basis of the cooperative representation classification model, a regularized weighting based on maximum likelihood estimation is proposed in this paper. A cooperative representation of the classification algorithm and its model. The algorithm realizes the adaptive weight distribution of the facial expression pixels and reduces the recognition interference of the boundary pixels by the weighted iterative analysis of the cooperative representation of the fidelity term. By using the cooperative representation of the fidelity term as the maximum likelihood estimation, the combined residual error is minimized and the facial expression recognition is effectively improved. The effectiveness of the system. (3) from the perspective of Bayesian estimation, this paper proposes a regularized weighted cooperative representation classification algorithm and its model based on the maximum a posteriori estimation. By introducing a maximum a posteriori estimation and a priori factor, the algorithm realizes the multi angle and multi-level evaluation of the facial expression recognition system. A Newton iterative estimation of the distribution parameters of a priori factor is used to effectively simplify the algorithm complexity of the facial expression recognition system. The regularized weighted cooperative representation based on maximum likelihood estimation and its model improve the accuracy and adaptability of facial expression recognition, and the regularized weighted cooperative table based on the maximum posterior estimation The classification algorithm and its model realize multi angle and multi-level evaluation of facial expression recognition system. The above research provides an effective mechanism for the cooperation model of facial expression recognition, and provides a high precision and robust algorithm, which lays a solid foundation for the practical application of face surface recognition system.
【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

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