本文選題：嵌入式系統(tǒng) + 人臉識別��； 參考：《杭州電子科技大學(xué)》2017年碩士論文

【摘要】：人臉識別作為一種友好的生物特征識別方式,具有不易偽造,容易獲取、準(zhǔn)確率高等優(yōu)點。傳統(tǒng)的人臉識別通常是在PC平臺上實現(xiàn)的,近幾年隨著硬件性能的提升,嵌入式開發(fā)板逐漸用于實現(xiàn)人臉識別。由于嵌入式開發(fā)板的便攜性好、穩(wěn)定性高等優(yōu)點,使嵌入式人臉識別系統(tǒng)的應(yīng)用領(lǐng)域十分廣泛。嵌入式開發(fā)板資源有限,可使用的算法具有一定的局限性。近幾年隨著人工智能技術(shù)的推廣,高識別率的深度學(xué)習(xí)方法應(yīng)用越來越廣泛,但卻無法直接運用在嵌入式開發(fā)板上。本文針對上述問題主要做了以下工作:(1)深入研究了嵌入式人臉識別的國內(nèi)外發(fā)展和研究現(xiàn)狀,總結(jié)了近幾年深度學(xué)習(xí)的算法,并詳細(xì)分析了其網(wǎng)絡(luò)結(jié)構(gòu),結(jié)合嵌入式開發(fā)板的硬件資源有限的特點討論了算法計算量。(2)研究了嵌入式人臉識別的各個組成部分。詳細(xì)分析了基于嵌入式人臉識別常用的方法,討論了常用的人臉數(shù)據(jù)庫,研究了照片存儲的格式,考慮了嵌入式人臉識別的耗時。(3)搭建了嵌入式人臉識別的開發(fā)環(huán)境。硬件方面,選擇ARM架構(gòu)的嵌入式開發(fā)板和攝像頭;軟件方面,選擇開源的Linux操作系統(tǒng)。在PC機上進行了虛擬機的安裝,建立了交叉編譯環(huán)境,在開發(fā)板上進行了內(nèi)核、驅(qū)動程序等相關(guān)的移植,為人臉識別應(yīng)用程序的設(shè)計和開發(fā)搭建了一個穩(wěn)定的運行環(huán)境。(4)提出了一種基于深度學(xué)習(xí)的pooling操作和DeepID算法的嵌入式系統(tǒng)pooling_patch算法。通過在ORL人臉庫和本實驗室成員的人臉圖像庫上進行實驗,結(jié)果表明該方法不僅識別率高,且耗時短。
[Abstract]:As a kind of friendly biometric recognition method, face recognition has the advantages of difficult to forge, easy to obtain, high accuracy and so on. Traditional face recognition is usually implemented on PC platform. With the improvement of hardware performance, embedded development board is gradually used to realize face recognition in recent years. Because of the good portability and high stability of the embedded development board, the embedded face recognition system has a wide range of applications. The resources of embedded development board are limited, and the algorithm that can be used has some limitations. In recent years, with the popularization of artificial intelligence technology, the deep learning method with high recognition rate is more and more widely used, but it can not be directly used on the embedded development board. In this paper, the following work is done to solve the above problems: (1) the development and research status of embedded face recognition at home and abroad are deeply studied, the algorithms of depth learning in recent years are summarized, and the network structure of embedded face recognition is analyzed in detail. Combined with the limited hardware resources of the embedded development board, the computational complexity of the algorithm is discussed. (2) the components of embedded face recognition are studied. The common methods based on embedded face recognition are analyzed in detail, the commonly used face database is discussed, the format of photo storage is studied, and the time consuming of embedded face recognition is considered. Hardware, choose the ARM architecture of embedded development board and camera; software, choose the open source Linux operating system. The installation of virtual machine on PC, the establishment of cross-compiling environment, the transplantation of kernel, driver and other related programs on the development board are carried out. For the design and development of face recognition application, a stable running environment is built. (4) an embedded system pooling_patch algorithm based on deep learning pooling operation and DeepID algorithm is proposed. The experiments on ORL face database and our lab face image database show that this method not only has a high recognition rate but also takes a short time.
【學(xué)位授予單位】：杭州電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41

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