本文選題：深度學習 + 人臉特征點定位��； 參考：《北京郵電大學》2016年碩士論文

【摘要】：人臉識別作為一種重要的生物特征識別技術,在刑偵、金融、企業(yè)管理、智能交通等眾多領域都有著廣泛的應用前景。但要把人臉識別技術真正運用到實際中,還有諸多問題需要解決。例如,人臉特征點的精確定位,姿態(tài)變化、低分辨率和部分遮擋等條件下的人臉識別問題等。近年來,隨著深度學習方向的研究不斷深入,其在人臉識別領域也得到了廣泛運用,在眾多公開數據集上取得了突破性的研究成果。由于它強大的表達能力,能夠擬合各種非線性函數,因此對于解決上述問題有著極大的優(yōu)勢。本文正是在此背景下,分別對現(xiàn)有深度學習在人臉特征點定位、人臉識別上的算法做深入分析與總結,并提出了基于多任務學習(multi-task learning)深度卷積網絡的人臉特征點定位算法以及基于多模態(tài)表示(multimodal representation)深度卷積網絡的人臉識別算法,分別在實驗數據集上獲得了良好的性能表現(xiàn)。在人臉特征點定位方面,近兩年來比較流行的方法多采用級聯(lián)的深度模型作為基本框架,如CFCNN[67]和CFAN[68]網絡。然而,這種級聯(lián)(cascade)結構會使模型的訓練和預測效率降低,且并未考慮對人臉姿態(tài)變化的魯棒性。本文提出的基于多任務學習的深度卷積神經網絡(Deep Convolutional Neural Networks,DCNN)將人臉特征點定位問題作為主要任務,頭部姿態(tài)檢測任務作為輔助任務,對兩者用深度卷積神經網絡聯(lián)合學習,從而獲得人臉特征點定位對于頭部姿態(tài)的魯棒性,最終在AFLW[70]數據集上達到了與級聯(lián)結構相匹配甚至更高的特征點定位精度,以及更短的預測時間。在人臉識別方面,近年來DeepID[72]、FaceNet[13]等基于DCNN的模型取得了非常好的人臉識別效果,但仍然是人臉的單一模態(tài)表示,很難對抗姿態(tài)的變化。本文提出的基于多模態(tài)表示的深度卷積網絡主要有兩點改進:其一是用多個并行的深度卷積網絡分別提取全局、局部以及姿態(tài)恢復后的人臉圖像特征,從而能夠得到對姿態(tài)、部分遮擋等具有不變性的特征。其二是將堆疊自動編碼器(Stacked Auto-encoders,SAE)代替?zhèn)鹘y(tǒng)主成分分析(Principal Component Analysis,PCA)方法運用于特征的降維,以獲得更具非線性的特征變換。在LFW[13]和CASIA-WebFace[80]數據集上分別評測模型的人臉認證準確率與人臉辨識準確率,均優(yōu)于常規(guī)的DCNN模型。
[Abstract]:As an important biological feature recognition technology, face recognition has wide application prospects in many fields, such as criminal investigation, finance, enterprise management, intelligent transportation and so on. But there are many problems to be solved in real application of face recognition technology. For example, the precise location of face feature points, attitude change, low resolution and part. In recent years, with the in-depth study of the depth learning direction, it has also been widely used in the field of face recognition. It has made a breakthrough in many public data sets. Because of its strong expressive ability, it can fit all kinds of nonlinear functions, so it can solve the problem. In this context, this paper makes an in-depth analysis and summary of the existing deep learning algorithms on face feature point positioning, face recognition, and proposes a face feature location algorithm based on Multi-task learning deep convolution network and multi mode representation (multimoda). L representation) face recognition algorithms in deep convolution networks have achieved good performance in experimental data sets. In the aspect of face feature point location, cascaded depth models are used as the basic framework, such as CFCNN[67] and CFAN [68] networks. However, this cascade (cascade) structure will make it possible The training and prediction efficiency of the model is reduced, and the robustness of the face attitude change is not considered. The Deep Convolutional Neural Networks (DCNN) based on multi task learning (DCNN) takes the face feature point location as the main task, and the head attitude detection task is used as an auxiliary task to use the deep convolution task as an auxiliary task. The degree convolution neural network combines learning to obtain the robustness of face feature point positioning for the head posture, and finally achieves the location precision of feature points that match even the cascade structure on the AFLW[70] data set, as well as the shorter prediction time. In the face recognition, DeepID[72], FaceNet[13] and other DCNN based modules in recent years. It has a very good face recognition effect, but it is still a single modal representation of the face. It is difficult to resist the change of attitude. The proposed depth convolution network based on multimodal representation has two improvements: one is to use multiple parallel deep convolution networks to extract global, local and postpose facial images. In addition, the Stacked Auto-encoders (SAE) instead of the traditional principal component analysis (Principal Component Analysis, PCA) method is applied to the dimensionality reduction of the feature to obtain a more nonlinear feature transformation. In LFW[13] and CASIA-WebFace[80] numbers, the other is to replace the traditional principal component analysis (Principal, PCA) method. The accuracy of face authentication and the accuracy of face recognition are better than those of the conventional DCNN model.
【學位授予單位】：北京郵電大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TP391.41;TP18

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相關博士學位論文 前1條

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本文編號：2014914