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研究生:廖威傑
研究生(外文):Wei-Jie Liao
論文名稱:即時人臉辨識在KIOSK上之應用
論文名稱(外文):Real-Time Face Identification for KIOSK Application
指導教授:林道通
指導教授(外文):Daw-Tung Lin
口試委員:莊仁輝、陳謀琰、林道通
口試委員(外文):Jen-Hui Chuang、Mou-Yen Chen、Daw-Tung Lin
口試日期:2014-07-24
學位類別:碩士
校院名稱:國立臺北大學
系所名稱:資通科技產業碩士專班
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2014
畢業學年度:102
語文別:英文
論文頁數:39
中文關鍵詞:人臉辨識、支持向量機
外文關鍵詞:face identification、retinex、support vecator machine、local binary pattern
相關次數:
  • 被引用被引用:1
  • 點閱點閱:903
  • 評分評分:
  • 下載下載:79
  • 收藏至我的研究室書目清單書目收藏:0
本文使用了一套基於資料分群概念的系統SVM訓練來做人臉辨識系統。辨識人臉首先必須先偵測到人臉影像,利用一種迭代演算法AdaBoost來偵測眼睛,得到眼睛中心坐標後,將兩眼坐標帶入設計好的橢圓遮罩去切割出人臉部位,由於原圖的五官對比不夠明顯,為了增加人臉五官的對比強度,使用了Retinex這個演算法,過去此演算法常用於數位相機自動白平衡中,它除了具有色彩恆常性,亦包含了強化影像的效果,對於光線對影像所造成的影響,具有良好的處理效果,接著就是使用擷取可辨識的臉部特徵,我們使用對紋理特徵有良好效果的LBP 特徵,只在灰階上的運算, 速度較快可應用於即時系統上,最後使用SVM 分類器去訓練LBP所擷取的特徵來做辨識。
Face identification for security systems has become an important research subject. This thesis proposes a face identification system for application in area access control systems. Support vector machine (SVM) was employed to conduct face identification based on a data clustering method. Initially, the face was detected using the AdaBoost algorithm. An elliptical mask was then used to remove the non-face area of the image. If the contrast was insufficient to produce a full-featured image of the face, the image was enhanced using the Retinex algorithm. This algorithm corrects lighting condition and maintains color constancy. A local binary pattern (LBP) was used to capture the facial features because it positively affects the characteristics of the texture. Employing an LBP is simple and fast; therefore it can be appropriately applied in real-time systems. An SVM classifier was used to train LBP features to accomplish identification. The proposed system is proven to be applicable for access control with satisfactory correct identification accuracy.
Abstract
Contents
1 Introduction
1.1 Motivation
1.2 Related Literature
1.3 Thesis Organization
2 Description of Approach
2.1 System Overview
2.2 Face Detection Procedure
2.2.1 Harr Like Feature
2.2.2 Adaboost Algorithm
2.2.3 Normalization and Background Removal
2.3 Enhancement using the Retinex method
2.4 Local Binary Pattern Extraction
2.5 Support Vector Machine Classification
3 Implementation and Experimental Results
4 Conclusion and Future Work
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