本文選題：人臉識(shí)別　切入點(diǎn)：特征提取　出處：《安徽理工大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著計(jì)算機(jī)和信息技術(shù)的快速發(fā)展,人臉識(shí)別技術(shù)越來(lái)越受到重視。本文主要研究了人臉在不同光照、不同表情下的特征提取與識(shí)別的一些關(guān)鍵問(wèn)題,提出了一些改進(jìn)方法,并通過(guò)實(shí)驗(yàn)進(jìn)行了可靠性驗(yàn)證。針對(duì)LBP算法提取人臉圖像的表情特征信息時(shí)會(huì)丟失特殊的特征信息的缺點(diǎn),本文提出了多重局部二值模式的人臉表情識(shí)別方法(Multiple Local Binary Patterns,MLBP),該方法在保持LBP算法優(yōu)點(diǎn)的前提下,通過(guò)增加一位二值編碼,利用中心像素點(diǎn)作用以及鄰域像素點(diǎn)灰度值之間的關(guān)系,得出特征向量圖。實(shí)驗(yàn)結(jié)果表明MLBP算法比LBP算法描述的表情紋理圖像更加均勻,且識(shí)別率約提高10%。針對(duì)人臉表情圖像進(jìn)行紋理特征提取時(shí)的模塊大小劃分問(wèn)題,本文提出將MLBP算法與Harr小波分解相結(jié)合,該方法首先將表情圖像進(jìn)行Harr小波分解,得到四幅不同頻率的子圖像,然后對(duì)其中三幅圖像進(jìn)行MLBP特征提取,并將得到的特征值串聯(lián)形成表情圖像的特征向量。實(shí)驗(yàn)結(jié)果表明該方法比MLBP方法直接提取表情特征所產(chǎn)生的特征向量的維數(shù)減少了 25%,特征提取和識(shí)別的速率提高了,其中識(shí)別率約提高了 9%。人臉識(shí)別研究中的識(shí)別率容易受光照強(qiáng)度的影響。針對(duì)MLBP算法在光照變化時(shí)具有旋轉(zhuǎn)不變性,以及Gabor小波能提供空間位置、空間頻率的特性,本文提出了多重局部 Gabor 二值模式方法(Multiple Local Gabor Binary Pattern,M LGBP),該方法先對(duì)人臉圖像使用Gabor小波進(jìn)行變換處理,保留受光照影響較小的高頻部分,然后再采用MLBP算法對(duì)Gabor提取后的圖像采用分塊編碼,最后得到聯(lián)合直方圖序列,獲得豐富的局部特征信息。實(shí)驗(yàn)結(jié)果表明了該算法有效的降低了光照對(duì)識(shí)別率的影響,提高了光照不均勻時(shí)的人臉識(shí)別率,且在特征提取方面比Gabor等算法更加有效。
[Abstract]:With the rapid development of computer and information technology, more and more attention has been paid to face recognition technology. In this paper, some key problems of feature extraction and recognition under different illumination and expression are studied, and some improved methods are proposed. The reliability of the algorithm is verified by experiments. The LBP algorithm will lose the special feature information when extracting facial expression information from the face image. In this paper, a multiple Local Binary pattern algorithm for facial expression recognition is proposed. The method preserves the advantages of the LBP algorithm by adding a bit of binary coding. Using the relationship between the central pixel and the gray value of the neighboring pixel, the eigenvector graph is obtained. The experimental results show that the MLBP algorithm is more uniform than the expression texture image described by the LBP algorithm. And the recognition rate is increased by about 10%. Aiming at the problem of module size partition in facial expression image texture feature extraction, this paper proposes the combination of MLBP algorithm and Harr wavelet decomposition. This method firstly decomposes facial expression image by Harr wavelet decomposition. Four sub-images with different frequencies were obtained, and three of them were extracted by MLBP. The experimental results show that this method reduces the dimension of feature vectors generated by MLBP method and improves the speed of feature extraction and recognition. The recognition rate is increased by about 9%. The recognition rate of face recognition is easily influenced by illumination intensity. Aiming at the rotation invariance of MLBP algorithm when illumination changes, and the feature that Gabor wavelet can provide spatial position and frequency, the recognition rate of face recognition is easy to be influenced by illumination intensity. This paper presents a multiplex local Gabor binary mode method called multiple Local Gabor Binary pattern (MGBP). In this method, the face image is first processed by Gabor wavelet transform, which preserves the high-frequency part which is less affected by illumination. Then the MLBP algorithm is used to code the extracted Gabor images in blocks. Finally, the joint histogram sequence is obtained, and the local feature information is obtained. The experimental results show that the algorithm can effectively reduce the influence of illumination on the recognition rate. The face recognition rate of uneven illumination is improved, and the feature extraction is more effective than Gabor algorithm.
【學(xué)位授予單位】：安徽理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.41

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