本文選題：人臉識(shí)別 + 多尺度金字塔特征塊��； 參考：《吉林大學(xué)》2017年碩士論文

【摘要】：人臉識(shí)別作為一項(xiàng)具有挑戰(zhàn)性的課題和長(zhǎng)期存在的學(xué)術(shù)問題,在越來越多的方向被應(yīng)用,如:信息安全、金融、法律約束、門禁系統(tǒng)以及諸多智能領(lǐng)域。作為一種非接觸性、遠(yuǎn)距離、隱蔽性很強(qiáng)的生物識(shí)別手段,人臉識(shí)別技術(shù)能夠在現(xiàn)實(shí)場(chǎng)景中迅速地辨別出人類個(gè)體的身份。然而,人臉識(shí)別在實(shí)際使用中,所獲取到的人臉圖片經(jīng)常來源于復(fù)雜的環(huán)境,在圖片中可能充斥著光照變化,人臉表情的變化或者遮擋情況等等。在這些情況下,要正確識(shí)別待分類的人臉圖片對(duì)于計(jì)算機(jī)來說是一種巨大的挑戰(zhàn)和困難。本文利用HOG(Histogram of Oriented Gradients)特征的優(yōu)點(diǎn),提出了基于多尺度金字塔特征塊的人臉識(shí)別算法(Multi-Layer Pyramid Feature Blocks)。首先,對(duì)于已經(jīng)預(yù)處理好的人臉圖片如AR人臉庫或Yale人臉庫的圖片,本文對(duì)其進(jìn)行向上和向下降采樣,以模擬人眼在觀察物體時(shí)不同尺度下的觀察過程。這個(gè)過程的本質(zhì)是人為地增加了圖像信息的豐富程度,獲得了圖像在空間上更多維度的信息并且很好地模擬了人眼的工作過程。此外,人臉圖片的關(guān)鍵部位如眼睛、眉毛、鼻子、鼻梁、嘴巴等含有大量具有區(qū)分度的特征信息,同時(shí)這些部位之間的結(jié)構(gòu)性和全局性的信息也具有很強(qiáng)的區(qū)分度,也是人類在識(shí)別不同個(gè)體時(shí)最重要的區(qū)分依據(jù)。為了突出人臉圖片這些關(guān)鍵部位的特征信息以及相互之間的結(jié)構(gòu)信息,同時(shí)也為了弱化人臉圖片中的次要信息并增強(qiáng)人臉識(shí)別算法對(duì)于遮擋、人臉表情變換等情況的魯棒性,本文提出了人臉特征塊這一概念并使用融合的人臉特征塊特征作為一張人臉圖片的更有效表達(dá)方式以獲得整體和細(xì)節(jié)信息。在每層金字塔上,本文進(jìn)行了多組實(shí)驗(yàn)來測(cè)試特征塊數(shù)量、大小和位置等變量對(duì)于識(shí)別效果的影響并取出識(shí)別效果最理想的一組,從預(yù)處理好的圖片中提取HOG特征以獲取更具區(qū)分度和代表性的表達(dá)方式。緊接著,本文把每一張圖片的所有特征塊按照一定順序進(jìn)行融合以獲得更加有代表性和整體性的特征。除此之外,本文還使用多尺度金字塔來構(gòu)建鄰近圖(Neighbor Graph)并運(yùn)用于局部保留投影(Locality Preserving Projection)算法中,以減少特征的維度、使分類器不容易過度擬合,同時(shí)加快算法匹配過程的速度。最終,本文使用最近鄰分類器,在著名的人臉數(shù)據(jù)庫AR人臉庫和Yale人臉庫驗(yàn)證了提出的人臉識(shí)別算法具有良好的魯棒性和識(shí)別效果。
[Abstract]:As a challenging subject and a long-standing academic problem, face recognition has been applied in more and more fields, such as information security, finance, legal constraints, access control systems and many intelligent fields. As a non-contact, long distance and strong hidden biometric method, face recognition technology can quickly distinguish the identity of human individual in the real scene. However, in the practical use of face recognition, the obtained face images often come from the complex environment, and the images may be filled with changes of illumination, changes of facial expressions or occlusion, and so on. In these cases, it is a great challenge and difficulty for the computer to correctly recognize the face images to be classified. In this paper, we propose a multi-layer Pyramid Feature places based face recognition algorithm based on multi-scale pyramid feature blocks, taking advantage of the advantages of HOG(Histogram of Oriented radients.This paper proposes an algorithm for face recognition based on multi-scale pyramid feature blocks. First of all, the human face images which have been preprocessed, such as AR face database or Yale face database, are sampled up and down to simulate the observation process of human eyes at different scales. The essence of this process is to artificially increase the richness of image information, to obtain more dimensional information of the image in space and to simulate the working process of the human eye well. In addition, the key parts of the face image, such as eyes, eyebrows, nose, mouth and so on, contain a large amount of distinguishing feature information. At the same time, the structural and global information between these parts also has a strong degree of discrimination. It is also the most important basis for human beings to distinguish different individuals. In order to highlight the feature information of the key parts of the face image and the structure information between them, and to weaken the secondary information in the face image and enhance the robustness of the face recognition algorithm to the occlusion, facial expression transformation, etc. In this paper, the concept of face feature block is proposed, and the fused face feature block feature is used as a more effective representation of a face image to obtain the overall and detailed information. On each pyramid, we have carried out many experiments to test the effect of the number, size and position of feature blocks on the recognition effect, and take out the most ideal group. HOG features are extracted from preprocessed images to obtain more differentiated and representative expressions. Then, all feature blocks of each picture are fused in a certain order to obtain more representative and integrated features. In addition, this paper uses multi-scale pyramid to construct neighbor graph and applies it to local preserving projection location Preserving projection) algorithm to reduce the dimension of feature, make the classifier difficult to over-fit, and speed up the matching process of the algorithm. Finally, we use the nearest neighbor classifier to verify the robustness and recognition effect of the proposed face recognition algorithm in the famous face database AR face database and Yale face database.
【學(xué)位授予單位】：吉林大學(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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