本文選題：人臉識別 + 人臉驗證　； 參考：《湘潭大學》2017年碩士論文

【摘要】：人臉驗證是人臉識別研究問題中一個重點也是一個難點,因為最近幾年因特網(wǎng)技術的發(fā)展,如何快速的進行身份驗證以確保個人信息安全,已經(jīng)成為一個熱門話題。由于人臉驗證問題是身份驗證中比較重要的生物驗證方法,從而使人臉驗證成為了一個新的研究熱點。人臉驗證是一個二類驗證問題即給定一張人臉圖片把它和已知身份的人臉圖片進行對比并判斷兩張圖片是否是同一個人。本文主要是基于卷積神經(jīng)網(wǎng)絡研究的延展。在應對于人臉驗證問題時分成了限制性條件和非限制條件下兩種情形并且提出了兩種基于卷積神經(jīng)網(wǎng)絡的人臉驗證模型。主要的研究工作如下:1在基于Yale B數(shù)據(jù)庫和AR數(shù)據(jù)庫兩個人臉數(shù)據(jù)庫上構造了一個混合模型的卷積神經(jīng)網(wǎng)絡的人臉驗證方法。相比于以前人臉驗證方法,這種方法對人臉驗證操作進行了分段操作。并且使用了PCA降維和SVM的驗證分類操作。該方法相比于傳統(tǒng)方法,在限制性實驗環(huán)境下人臉驗證的準確率有較好的提升。2在應對非限制條件下人臉圖片時由于混合卷積神經(jīng)網(wǎng)絡的局限性,所以對上述的混合卷積神經(jīng)網(wǎng)絡進行了優(yōu)化和改進從而構造了一個并行三通道卷積神經(jīng)網(wǎng)絡結構的人臉驗證模型。3在LFW數(shù)據(jù)庫上構造了兩種不同的連接方式的三通道并行卷積神經(jīng)網(wǎng)絡模型。第一種采用了傳統(tǒng)的全連接的方式。第二種是使用了局部連接的方式。使用兩種連接的網(wǎng)絡是為了比較在不同的連接方式下那種連接方式可以提高準確率。為了提高整個模型的正確率,模型進行了兩次訓練。第一次使用了SGD的優(yōu)化函數(shù)進行首次訓練,并保存下最好結果的模型。第二次使用了Adadelta優(yōu)化函數(shù)在第一次訓練好的模型上進行了二次訓練以此來提高整個模型的準確率。
[Abstract]:Face verification is an important and difficult problem in face recognition, because with the development of Internet technology in recent years, how to quickly authenticate to ensure the security of personal information has become a hot topic.Face verification is an important biometric method in authentication, which makes human face verification become a new research hotspot.Face verification is a kind of verification problem that is to compare a face image with a face image of known identity and judge whether two images are the same person.This paper is mainly based on the extension of convolution neural network research.In this paper, the face verification problem is divided into two cases: restricted and unconstrained, and two kinds of face verification models based on convolution neural network are proposed.The main work of this paper is as follows: 1. Based on Yale B database and AR database, we construct a hybrid model based on convolution neural network for face verification.Compared with the previous face verification method, this method performs segmentation operation.And the verification classification operation of PCA reduction and SVM is used.Compared with the traditional method, the accuracy of face verification in the restricted experimental environment is better than that of the traditional one. 2. When dealing with face images under unconstrained conditions, the proposed method is limited by hybrid convolution neural networks.So the hybrid convolution neural network mentioned above is optimized and improved to construct a parallel three-channel convolutional neural network model of face verification. 3. 3 constructs two kinds of three different connection modes on LFW database.Channel parallel convolution neural network model.The first uses the traditional full-connected approach.The second is the use of local connections.Two kinds of connection networks are used to compare which connection modes can improve the accuracy of different connection modes.In order to improve the accuracy of the whole model, the model was trained twice.For the first time, the optimization function of SGD is used for the first time, and the best result model is saved.In the second time, the Adadelta optimization function is used to improve the accuracy of the model.
【學位授予單位】：湘潭大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41;TP183

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本文編號：1750425