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Author: 謝旻斈
Min-Shue Hsieh
Thesis Title: 結合多尺度空間與局部梯度特徵比對之手持式裝置人臉辨識系統
A Face Recognition System Based on Mobile Device using Multi-Scale and Local Gradient Feature
Advisor: 洪西進
Shi-Jinn Horng
Committee: 馮輝文
Huei-Wen Ferng
林韋宏
Wei-Hung Lin
Degree: 碩士
Master
Department: 電資學院 - 資訊工程系
Department of Computer Science and Information Engineering
Thesis Publication Year: 2015
Graduation Academic Year: 103
Language: 中文
Pages: 55
Keywords (in Chinese): 人臉辨識Android局部二元圖形影像前處理梯度方向卡方統計
Keywords (in other languages): face recognition, Android, local binary pattern, Image pre-processing, Chi square statistic, Gradient
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由於資訊日益發達造成安控問題隨之擴大,如以往般使用個人設置之密碼之作法已開始變得不安全。因此,利用具有獨具性的生物特徵代替密碼的比率也逐漸提高。在生物特徵中,人臉雖然比起指、掌靜脈甚至於指紋來說獨具性稍微弱了一點,但作為生物特徵來說,欲取得可用以辨識的人臉影像方式較為簡單,且不需使用接觸式的取像方法即可完成,故為目前生物特徵辨識方法的主流之ㄧ。本論文利用人臉辨識之特性,先剪取出人臉部分,再以影像前處理的方式去除光源對拍攝所帶來的影響,並對人臉採取經改良過後結合梯度方向(Gradient)之特徵擷取方法,最後使用卡方分配(Chi-Square)進行比對作為主要辨識之流程,並將此系統建立於Android手持裝置上,取得了良好的辨識效果。


Security problem is extending due to the developing of technology, the traditional security facility like setting password has become less and less unsafety. By this reason, the rate of using biometric characteristics to replace password had become higher and higher. In the kinds of biometric characteristics, although faces have less uniqueness than finger vein and palm vein, but it can be convenience of taking pictures and can finish the identification without touching the device, and that’s why face recognition becoming one of the major recognition techniques.

We propose a face recognition system based on Android system using haar-like feature to detect human face, and remove the impact of external factor by using image pre-processing algorithm, then extracting feature from face with a novel local binary patterns combining gradient information. And at last, we compute the similarity score with chi score and also combining the gradient information to increase the accuracy. Finally, we use many databases of face image as the experiment, and have achieved great recognition results.

摘要 Abstract 目錄 圖目錄 第一章 緒論 第二章 系統流程與使用硬體規格介紹 第三章 人臉辨識 第四章系統實作與效能 第五章 結論

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