串流影像之即時人臉偵測、追蹤與辨識─嵌入式系統設計

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何曉平(Hsiao-Ping H0) 查詢紙本館藏

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資訊工程學系

論文名稱

串流影像之即時人臉偵測、追蹤與辨識─嵌入式系統設計
(Real Time Face Detection, Tracking and Recognition for Streaming Images: Embedded System Design)

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摘要(中)

在現今科技發達且資訊流通快速的時代，以生物特徵辨識為基礎的身份認證方法已經是重要的趨勢，人臉辨識便是其中一項廣泛被應用的技術。為了因應各種可攜的(portable)、獨立平台(standalone)的人臉辨識需求，設計一個高性能嵌入式人臉辨識系統成為一個重要的課題。本論文提出一個融合水平投影特徵及幾何特徵等雙模態特徵為基礎的人臉偵測、追綜與辨識系統，利用人臉左右對稱特性，以及人臉獨特的五官幾何比例，融合此兩類特徵成為具有高鑑別度的分類特徵。
傳統人臉辨識研究大都是對單張靜態影像進行辨識，其辨識結果容易受到取像環境影響。本研究將人臉偵測、追蹤、辨識演算法結合應用在連續串流影像，可持續對同一個人進行多次人臉特徵比對，提供更可靠的人臉身份辨識。我們首先利用膚色模型以及人臉形態學比例篩選出人臉區域；接著以粒子群最佳化演算法追蹤移動人臉位置；再擷取偵測的人臉目標影像的雙模態特徵向量；最後利用機率式神經網路進行人臉辨識。
我們分別對人臉偵測、追蹤以及辨識演算法進行功能驗證和實驗。人臉偵測部份比起傳統的Viola-Jones detector有較佳的偵測率以及複雜影像環境容忍度；人臉追蹤部份使用粒子群最佳化演算法(PSO，Particle Swarm Optimization)搜索局部區域人臉位置，比起每次都對整張影像作全域搜尋有更快的執行速度以及正確性較好的搜尋結果；人臉辨識部份相較於特徵臉(eigenface)演算法，有較佳的相等錯誤率(EER)辨識結果以及更少的特徵資料庫儲存空間需求。
最後將本系統在個人電腦以及嵌入式處理器NIOS II平台上完成嵌入式軟體的設計和驗證，並分析系統效能，找出效能瓶頸的功能模組，再依照MIAT機器智慧自動化技術實驗室提出的硬體設計方法論，將其實作為嵌入式硬體，以提昇整體系統效能。

摘要(英)

In recent years, using biometric features to recognize people has becoming an important topic. This thesis proposed a high performance embedded face recognition system, based on geometric feature/projection feature biometric decision fusion.
We perform our face recognition system on continuous stream images. Most of the traditional research implement their face recognition algorithm on static images, therefore, the final result would depend on the image quality. We can identify a person in stream image many times, and getting a more reliable result. First, we uses the facial color filter and the ellipse mask to partition the possible facial areas, then we using the Particle Swarm Optimization to locate the facial position, After extracting the bi-modal biometrics from human face, we finally recognize human by Probabilistic Neural Network(PNN).
We compare our face detection subsystem with Viola-Jones detector, the experiment reveals a better detection rate and tolerance of bad image quality. Besides, both the operated speed and detection rate, using Particle Swarm Optimization tracking is better than performing detection on every single image. We compare our face recognition subsystem with eigenface algorithm, the former has better EER result and less database storage requirement than the latter.
In this thesis , we verify our face detection、tracking and recognition system on personal computer and embedded platform. Furthermore, according to the MIAT methodology , the system is divided into independent modules, we analyze every modules to find system bottleneck , and then implementing such modules on embedded hardware .

關鍵字(中)

★ 嵌入式系統
★ 人臉辨識
★ 人臉偵測

關鍵字(英)

★ FPGA
★ PNN
★ PSO
★ face detection
★ face recognition

論文目次

摘要 i
Abstract ii
目錄 iii
圖目錄 v
表目錄 viii
第一章、緒論 1
1.1研究動機 1
1.2相關文獻探討 2
1.2.1人臉偵測方法回顧 2
1.2.2人臉追蹤方法回顧 4
1.2.3人臉辨識方法回顧 7
1.3系統流程與狀態組織 9
第二章、人臉偵測與追蹤 11
2.1人臉偵測 11
2.2 PSO人臉追蹤 21
2.2.1 PSO原理 21
2.2.2 PSO人臉位置追蹤 23
2.3實驗結果 27
第三章、人臉特徵抽取與辨識 34
3.1人臉特徵抽取 34
3.1.1幾何特徵抽取 34
3.1.2水平投影特徵抽取 46
3.2機率式神經網路分類器 52
3.2.1貝式決策定理 52
3.2.2 Parzen視窗法 53
3.2.3 PNN架構 55
3.3實驗結果 57
第四章、嵌入式系統設計實作 63
4.1高階系統硬體設計方法論 63
4.2系統架構 67
4.3嵌入式系統設計 71
4.4硬體合成 74
4.5實驗結果 100
第五章、結論與未來工作 102
5.1結論 102
5.2未來工作 103
參考文獻 104

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指導教授

陳慶瀚(GING-HAN CHEN)

審核日期

2010-7-26

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