人臉識別中光照預(yù)處理算法研究
發(fā)布時間:2018-11-16 11:16
【摘要】:人臉識別,是生物特征識別領(lǐng)域中的熱門研究話題,同時也是計(jì)算機(jī)視覺領(lǐng)域最成功的應(yīng)用之一。它具有廣泛的應(yīng)用前景,在門禁系統(tǒng),智能安防,智能監(jiān)控以及國家軍事和安全領(lǐng)域等表現(xiàn)出了無可替代的作用和潛力。通過將近五十年的研究,該技術(shù)目前已經(jīng)取得了很大的進(jìn)步,并且投入到商業(yè)使用。但是總體而言,人臉識別仍然存在一些難題,比如在光照條件差,用戶配合度低的情況下,識別性能將會迅速降低。因此本文綜合分析了其中一個因素即光照變化給人臉識別帶來的影響,并對其深入研究,提出改進(jìn)算法。本文詳細(xì)分析了基于圖像處理的基本算法與基于朗伯模型的光照不變量提取算法,并且根據(jù)理論分析結(jié)果提出了兩種改進(jìn)算法。本文提出的第一種算法是一種基于維納濾波的自商圖像算法。自商圖像算法克服了傳統(tǒng)商圖像的局限性,通過自商模型可以提取與光照無關(guān)的內(nèi)在特性,但是該算法對人臉表面光照的估計(jì)使用高斯濾波。這類算法沒有考慮到該濾波器在平滑人臉表面的同時也將輪廓特征模糊化,而本文采用的自適應(yīng)維納濾波能夠根據(jù)人臉表面局部方差值自動調(diào)整濾波強(qiáng)度,從而更好的獲取人臉的本質(zhì)特征表達(dá)。本文提出的第二種算法是基于多方向的相對梯度算法。文中通過理論驗(yàn)證得到圖像的相對梯度特征也具有光照不變性,傳統(tǒng)梯度圖像的計(jì)算僅僅考慮X和Y兩個方向的梯度分量,而本文綜合考慮了四個方向,通過高斯函數(shù)一階導(dǎo)數(shù)與圖像作卷積計(jì)算出多個方向的梯度分量,然后對每個方向的相對梯度分量作加權(quán)融合,從而獲得更好的人臉圖像本質(zhì)特征表達(dá)。本文提出的兩種改進(jìn)算法分別都在Extended Yale B和CMU PIE人臉庫上做實(shí)驗(yàn),同時本文的算法同多個光照處理算法作了對比,統(tǒng)一使用最近鄰作為分類標(biāo)準(zhǔn)。大量的實(shí)驗(yàn)結(jié)果表明,本文提出的算法能夠有效的獲取光照不變分量,提高人臉識別準(zhǔn)確率。
[Abstract]:Face recognition is a hot topic in biometric recognition field, and it is also one of the most successful applications in computer vision field. It has a wide application prospect, and has shown irreplaceable role and potential in access control system, intelligent security, intelligent monitoring and national military and security fields. Through nearly 50 years of research, the technology has made great progress and put into commercial use. But in general, there are still some problems in face recognition, such as poor lighting conditions and low user cooperation, the performance of face recognition will be reduced rapidly. Therefore, this paper comprehensively analyzes one of the factors, that is, the influence of illumination change on face recognition, and makes a thorough study of it, and proposes an improved algorithm. In this paper, the basic algorithm based on image processing and the illumination invariant extraction algorithm based on Lambert model are analyzed in detail, and two improved algorithms are proposed according to the results of theoretical analysis. The first algorithm proposed in this paper is a self-quotient image algorithm based on Wiener filter. The self-quotient image algorithm overcomes the limitation of the traditional quotient image and can extract the inherent characteristics independent of illumination through the self-quotient model. But Gao Si filter is used to estimate the illumination of the face surface. This algorithm does not take into account that the filter not only smoothes the face surface but also blurs the contour features, and the adaptive Wiener filter can automatically adjust the filtering intensity according to the local square difference of the face surface. In order to obtain a better expression of the essential features of the face. The second algorithm proposed in this paper is a multi-directional relative gradient algorithm. In this paper, the relative gradient features of the image are also shown to be illumination invariant by theoretical verification. The traditional gradient images only consider the gradient components in X and Y directions, and the four directions are considered in this paper. The gradient components of multiple directions are calculated by convolution of the first derivative of Gao Si function with the image, and then the relative gradient components of each direction are weighted and fused to obtain a better expression of the essential features of the face image. The two improved algorithms proposed in this paper are experimented on Extended Yale B and CMU PIE face database respectively. The proposed algorithm is compared with several illumination processing algorithms and the nearest neighbor is used as the classification standard. A large number of experimental results show that the proposed algorithm can effectively obtain illumination invariant components and improve the accuracy of face recognition.
【學(xué)位授予單位】:哈爾濱工業(yè)大學(xué)
【學(xué)位級別】:碩士
【學(xué)位授予年份】:2017
【分類號】:TP391.41
[Abstract]:Face recognition is a hot topic in biometric recognition field, and it is also one of the most successful applications in computer vision field. It has a wide application prospect, and has shown irreplaceable role and potential in access control system, intelligent security, intelligent monitoring and national military and security fields. Through nearly 50 years of research, the technology has made great progress and put into commercial use. But in general, there are still some problems in face recognition, such as poor lighting conditions and low user cooperation, the performance of face recognition will be reduced rapidly. Therefore, this paper comprehensively analyzes one of the factors, that is, the influence of illumination change on face recognition, and makes a thorough study of it, and proposes an improved algorithm. In this paper, the basic algorithm based on image processing and the illumination invariant extraction algorithm based on Lambert model are analyzed in detail, and two improved algorithms are proposed according to the results of theoretical analysis. The first algorithm proposed in this paper is a self-quotient image algorithm based on Wiener filter. The self-quotient image algorithm overcomes the limitation of the traditional quotient image and can extract the inherent characteristics independent of illumination through the self-quotient model. But Gao Si filter is used to estimate the illumination of the face surface. This algorithm does not take into account that the filter not only smoothes the face surface but also blurs the contour features, and the adaptive Wiener filter can automatically adjust the filtering intensity according to the local square difference of the face surface. In order to obtain a better expression of the essential features of the face. The second algorithm proposed in this paper is a multi-directional relative gradient algorithm. In this paper, the relative gradient features of the image are also shown to be illumination invariant by theoretical verification. The traditional gradient images only consider the gradient components in X and Y directions, and the four directions are considered in this paper. The gradient components of multiple directions are calculated by convolution of the first derivative of Gao Si function with the image, and then the relative gradient components of each direction are weighted and fused to obtain a better expression of the essential features of the face image. The two improved algorithms proposed in this paper are experimented on Extended Yale B and CMU PIE face database respectively. The proposed algorithm is compared with several illumination processing algorithms and the nearest neighbor is used as the classification standard. A large number of experimental results show that the proposed algorithm can effectively obtain illumination invariant components and improve the accuracy of face recognition.
【學(xué)位授予單位】:哈爾濱工業(yè)大學(xué)
【學(xué)位級別】:碩士
【學(xué)位授予年份】:2017
【分類號】:TP391.41
【參考文獻(xiàn)】
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