基於稀疏表示之人臉驗證與唇語辨識系統; Face Verification and Lip Reading Systems based on Sparse Representation

NCUIR > College of Electrical Engineering & Computer Science > Graduate Institute of Computer Science and Information Engineering > Electronic Thesis & Dissertation > Item 987654321/61583

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/61583

Title:	基於稀疏表示之人臉驗證與唇語辨識系統;Face Verification and Lip Reading Systems based on Sparse Representation
Authors:	許徑嘉;Hsu,Ching-chia
Contributors:	資訊工程學系
Keywords:	稀疏表示;sparse representation
Date:	2013-08-26
Issue Date:	2013-10-08 15:22:57 (UTC+8)
Publisher:	國立中央大學
Abstract:	人臉驗證的應用範圍很廣，如何將其用於真實世界一直是眾多學者研究的議題，我們對人臉擷取SIFT參數，其對於旋轉、平移和尺度皆有不變的特性，並用其來建立稀疏表示的字典，藉由K-means以及資訊理論，我們提出兩種擴增字典的方法，實驗結果顯示，藉由擴增字典，可以有效的增加稀疏係數的稀疏性，並改善驗證率以及重建訊號的殘餘值。本論文利用BCS求解最佳化問題，相較於以往的OMP演算法，BCS除了求解最佳化問題外，所獲得的共變異數可以用於改善遞增字典，以降低觀測向量的不確定性，實驗結果顯示，遞增字典確實可使重建訊號的殘餘值減少。傳統唇語辨識都是用ASM或AAM取得唇形作為參數，可能會遺失部分有用的資訊，本論文考慮唇語的整體影像，利用SIFT作為參數，藉由BOF，可以將多個SIFT特徵點轉化為向量，並利用其訓練HMM模型。我們測試英文字母A~Z，其實驗結果也好於Baseline系統。 Face verification has many applications. The critical problem which lots of researchers concern is how to apply to real-world. In order to robust orientation, translation and scaling of face images, we extract SIFT features of face images which is built dictionary of sparse representation. We propose two kinds of method to extend dictionary via K-means and information theory(extended dictionary and incremental dictionary). Experiments show that we can increase sparseness of sparse coefficients efficiently, also can improve verification rate and reconstruction error via extended dictionary. This paper utilize BCS to solve optimization problem. Compare to OMP algorithm, BCS not only can solve optimization problem but also can improve dictionary by covariance which can decrease uncertainty of observation vectors. Experiments show that incremental dictionary do increases residual of reconstruction error. Lip reading has utilized ASM or AAM as features past few years. We concern that it might lose some useful information, therefore we consider whole image information by extracting SIFT features. In order to train HMM model via SIFT features, we utilize BOF to transform matrices of SIFT features into vectors. We experiment letters A-Z, and the result show that performance of proposed method is better than baseline systems.
Appears in Collections:	[Graduate Institute of Computer Science and Information Engineering] Electronic Thesis & Dissertation

Files in This Item:

File	Description	Size	Format
index.html		0Kb	HTML	667	View/Open

社群 sharing

Loading...