【摘要】：在當今信息化時代,如何準確鑒定一個人的身份、保護信息安全,已成為一個必須解決的關(guān)鍵社會問題。人臉識別技術(shù)是最有發(fā)展?jié)摿Φ纳锾卣髯R別技術(shù)之一,由于其具有簡單直觀、不易復(fù)制、安全性高、操作容易等特點而在身份驗證和識別場合具有巨大的應(yīng)用價值和廣闊的應(yīng)用前景。而特征提取的好壞對人臉的正確識別有著至關(guān)重要的影響,因此,如何提取穩(wěn)定有效的人臉特征,使得提取的特征盡可能多的含有有利于識別的類別信息,以及如何將多種不同的特征相結(jié)合來實現(xiàn)更為理想的分類結(jié)果等都是現(xiàn)階段人臉識別的研究熱點。通過對現(xiàn)有人臉識別相關(guān)文獻的閱讀,本文在總結(jié)前人研究成果的基礎(chǔ)上,深入研究了基于局部二值模式和深度學(xué)習(xí)的特征提取方法,并在此基礎(chǔ)上做了以下工作:(1)針對選用一種特征不足以捕捉人臉圖像多方面的識別信息問題,綜合考慮局部二值模式(LBP)的改進算法ELBP與離散余弦變換(DCT)的優(yōu)缺點,提出了一種將ELBP與離散余弦變換(DCT)相結(jié)合來進行特征提取的方法,該方法將人臉圖像經(jīng)DCT變換后的少量低頻系數(shù)作為人臉的頻域特征,將人臉圖像中眼部和嘴部區(qū)域的ELBP特征作為人臉的空域特征,并使用PCA方法對所提取的空頻域特征進行有效融合,得到更有效的人臉特征,通過在ORL人臉庫和Yale人臉庫上的實驗驗證了該方法的有效性。(2)針對深度信念網(wǎng)絡(luò)(DBNs)忽略了圖像局部結(jié)構(gòu),難以學(xué)習(xí)到人臉圖像的局部特征以及網(wǎng)絡(luò)訓(xùn)練時間過長等問題進行了研究,提出將LBP特征作為DBNs的輸入,并在DBNs的訓(xùn)練過程中引入極限學(xué)習(xí)機(ELM)來加快DBNs的訓(xùn)練速度,最后用訓(xùn)練好的網(wǎng)絡(luò)進行分類識別。在ORL人臉庫和FERET人臉庫上對不同樣本規(guī)模和不同分辨率的圖像進行實驗,結(jié)果表明:與單獨采用LBP或DBNs提取特征的方法相比,該方法取得了較好的學(xué)習(xí)效率和識別效果。
[Abstract]:In today's information age, how to accurately identify a person and protect information security has become a key social problem that must be solved. Face recognition is one of the most promising biometric recognition techniques. Because of its easy operation, it has great application value and broad application prospect in authentication and identification. The quality of feature extraction plays an important role in correct face recognition. Therefore, how to extract stable and effective face features makes the extracted features contain as many kinds of information as possible. And how to combine a variety of different features to achieve a more ideal classification results are now the focus of face recognition research. On the basis of summarizing the previous research results, this paper deeply studies the feature extraction method based on local binary pattern and depth learning. On this basis, the following work has been done: (1) aiming at the problem of selecting a feature that is not sufficient to capture face image in many aspects, Considering the advantages and disadvantages of the improved algorithm of local binary mode (LBP) (ELBP) and discrete cosine transform (DCT), a method of feature extraction by combining ELBP with discrete cosine transform (DCT) is proposed. In this method, a small amount of low frequency coefficients after DCT transform are used as the frequency domain features of the face, and the ELBP features of the eye and mouth regions in the face image are taken as the spatial features of the face. PCA method is used to effectively fuse the extracted space-frequency domain features to obtain more effective facial features. Experiments on ORL face database and Yale face database demonstrate the effectiveness of the method. (2) the local image structure is ignored in depth belief network (DBNs). It is difficult to learn the local features of face images and the network training time is too long to study. It is proposed that the LBP feature be taken as the input of DBNs, and the extreme learning machine (ELM) is introduced in the process of DBNs training to speed up the training speed of DBNs. Finally, the trained network is used to classify and identify. Experiments on images with different sample sizes and different resolutions are carried out on ORL and FERET face databases. The results show that compared with the methods using LBP or DBNs alone, this method has better learning efficiency and recognition effect.
【學(xué)位授予單位】：蘭州理工大學(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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