以核心模糊最近特徵線轉換法做人臉辨識; Kernel Fuzzy Nearest Feature Line Embedding for Face Recognition

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Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/64325

Title:	以核心模糊最近特徵線轉換法做人臉辨識;Kernel Fuzzy Nearest Feature Line Embedding for Face Recognition
Authors:	蔡宗豪;Cai,Zong-Hao
Contributors:	資訊工程學系在職專班
Keywords:	最近特徵線轉換法;核心;模糊;人臉辨識;Nearest Feature Line Embedding;Kernel;Fuzzy;Face Recognition
Date:	2014-03-18
Issue Date:	2014-06-19 14:06:52 (UTC+8)
Publisher:	國立中央大學
Abstract:	本篇論文主要是以最近特徵線轉換(Nearest Feature Line Embedding, NFLE) 為基礎，將Fuzzy NFLE (FNFLE)、Kernel NFLE (KNFLE) 、以及在本研究中所提出的Kernel Fuzzy NFLE (KFNFLE)，三種方法進行效能比較和結果分析，進而了解KNFLE、FNFLE 以及 KFNFLE 應用在人臉辨識的實際結果。本研究的動機是認為Kernel 與Fuzzy 方法皆能強化特徵空間，因此在我們所提出KFNFLE 中，Kernel 與Fuzzy 方法同時被用來改善 NFLE 的效能。首先，人臉訓練樣本先轉換至PCA 空間，接著再以訓練樣本去求得FNFLE、KNFLE 與KFNFLE 的轉換矩陣，最後再將訓練樣本與測試樣本透過轉換矩陣投影至新的特徵空間中，並以最近鄰居法進行比對。實驗以CMU 人臉資料庫與自製人臉資料庫進行，實驗結果顯示，當樣本數增加時，我們所提出的KFNFLE 的效果如預期優於其它演算法。; In this thesis, three algorithms based on Nearest Feature Line Embedding (NFLE) including Fuzzy NFLE (FNFLE), Kernel NFLE (KNFLE),and the proposed Kernel Fuzzy NFLE (KFNFLE) methods are implemented to demonstrate their effectiveness applying on face recognition. The motivation of this study relies main by on the fact that both Kernel method and Fuzzy model can enhance the transformed feature space. Therefore, Kernel method and Fuzzy model are both considered in the proposed KFNFLE to further improve the performance of original NFLE. Firstly, the training faces are transformed by applying PCA method. Then, the transformed matrixes based on FNFLE, KNFLE, and KFNFLE are obtained, respectively. Next, the prototype and testing samples are projected onto the feature space via the obtained transformed matrixes. Last, nearest neighbor method is applied for matching. In the experiments, the CMU face database, and our real-case face database are utilized to evaluate the performance of the proposed KFNFLE method. Experimental results demonstrate that the performance of the proposed KFNFLE is superior to FNFLE, KNFLE, and original NFLE when the sample size increases.
Appears in Collections:	[Executive Master of Computer Science and Information Engineering] Electronic Thesis & Dissertation

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