【摘要】：人臉識別作為少數(shù)幾個同時具有高精度和低干涉的生理特征識別方法,在數(shù)字身份認(rèn)證、公共安全、多媒體等領(lǐng)域具有重要的應(yīng)用價值。目前,在控制配合條件下的人臉識別系統(tǒng)能夠取得較高的識別率,但當(dāng)人臉存在姿態(tài)變化時,同時是單視圖時,人臉識別系統(tǒng)面臨巨大挑戰(zhàn)。本文圍繞單視圖多姿態(tài)的人臉識別方法進(jìn)行了系統(tǒng)研究,具體工作和主要成果包括:1、提出—種基于線性回歸算法與支持向量機相結(jié)合的方法。針對待識別人臉存在姿態(tài)變化,基于線性回歸算法尋求正、側(cè)人臉之間的關(guān)系,然后利用此關(guān)系進(jìn)行姿態(tài)校正。最后,利用支持向量機在小樣本分類上的優(yōu)勢,采用遺傳算法篩選其參數(shù),對校正后的待識別人臉進(jìn)行分類識別。在CAS-PEAL-R1人臉庫上,識別率達(dá)86%。實驗結(jié)果表明,該方法在處理基于單視圖多姿態(tài)的人臉識別問題時,識別率高于同類其它方法。2、提出一種基于單張三維人臉重建生成虛擬多視圖的方法。針對訓(xùn)練樣本不足的問題,借助基于稀疏形變模型的三維人臉重建方法,重建輸入人臉的三維人臉模型,然后通過紋理映射、旋轉(zhuǎn)、投影等方法獲取輸入人臉的多姿態(tài)人臉圖像,豐富訓(xùn)練樣本庫。在此基礎(chǔ)上,利用BP神經(jīng)網(wǎng)絡(luò)進(jìn)行人臉識別。在CAS-PEAL-R1人臉庫上,識別率達(dá)91%。實驗結(jié)果表明,該方法生成的虛擬多視圖提高了識別效果,識別率高于同類其它方法。3、在上述工作的基礎(chǔ)上,設(shè)計并實現(xiàn)了基于Matlab的人臉識別系統(tǒng)。該系統(tǒng)集成了本文所提的兩種方法。
[Abstract]:Face recognition, as a few physiological feature recognition methods with high precision and low interference at the same time, has important application value in digital identity authentication, public security, multimedia and other fields. At present, the face recognition system can achieve a high recognition rate under the condition of control and coordination, but when the face posture changes and it is a single view, the face recognition system faces great challenges. In this paper, the face recognition method based on single view and multi-pose is studied systematically. the concrete work and main results are as follows: 1. A method based on linear regression algorithm and support vector machine (SVM) is proposed. Aiming at the attitude change of other people's faces, the relationship between positive and lateral faces is found based on linear regression algorithm, and then the attitude correction is carried out by using this relationship. Finally, using the advantages of support vector machine in small sample classification, genetic algorithm is used to screen its parameters, and the corrected human face is classified and recognized. On CAS-PEAL-R1 face database, the recognition rate is 86%. The experimental results show that the recognition rate of this method is higher than that of other similar methods when dealing with the problem of face recognition based on single view and multi-pose. 2, a virtual multi-view method based on single 3D face reconstruction is proposed. In order to solve the problem of insufficient training samples, the 3D face model of input face is reconstructed with the help of 3D face reconstruction method based on sparse deformation model, and then the multi-pose face image of input face is obtained by texture mapping, rotation, projection and so on, which enriches the training sample database. On this basis, BP neural network is used for face recognition. On CAS-PEAL-R1 face database, the recognition rate is 91%. The experimental results show that the virtual multi-view generated by this method improves the recognition effect, and the recognition rate is higher than that of other similar methods. 3. On the basis of the above work, a face recognition system based on Matlab is designed and implemented. The system integrates the two methods proposed in this paper.
【學(xué)位授予單位】：西安理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：TP391.41

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