臺灣博碩士論文加值系統

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.

摘要 i
Abstract ii
圖目錄 iii
表目錄 v
章節目次 vi
第1章 緒論 - 1 -
1.1 前言 - 1 -
1.2 研究動機與目的 - 2 -
1.3 論文架構 - 3 -
第2章 文獻探討 - 5 -
2.1 Eigenface和Fisherface - 5 -
2.2 區域保留投影(Locality Preserving Projection, LPP) - 6 -
2.3 Histogram of Gabor Phase Pattern(HGPP) - 6 -
2.4 區域二元特徵(Local Binary Patterns, LBP) - 7 -
2.5 分類器(Classifier) - 7 -
第3章 稀疏表示(Sparse Representation) - 8 -
3-1 稀疏表示問題 - 8 -
3-2 應用於人臉辨識之稀疏表示問題 - 9 -
第4章 研究方法 - 12 -
4-1 貝式壓縮感測(Bayesian Compressive Sensing) - 12 -
4-1-1 稀疏事前機率(Sparseness Prior) - 12 -
4-1-2 透過Relevance Vector Machine估測稀疏係數 - 13 -
4-2 SIFT特徵參數 - 16 -
4-2-1 Detect scale-space extrema - 17 -
4-2-2 Keypoint localization - 20 -
4-2-3 Orientation assignment and Generate image descriptor - 21 -
4-3 建立字典 - 22 -
4-4 人臉驗證 - 23 -
4-5 擴增字典 - 25 -
4-5-1 K-Means群聚演算法 - 25 -
4-5-2 利用K-means建立擴增字典 - 26 -
4-6 人臉驗證演算法 - 28 -
4-7 遞增字典(Incremental Dictionary) - 29 -
第5章 唇語辨識 - 32 -
5-1 Bag-of-Features(BOF) - 32 -
5-1-1 BOF應用於SIFT特徵參數 - 33 -
5-2 隱藏馬可夫模型 - 35 -
5-2-1 向前演算法(Forward Algorithm) - 37 -
5-2-2 EM演算法 - 37 -
5-3 Bayesian Sensing Hidden Markov Model - 39 -
第6章 實驗結果 - 40 -
6-1 Baseline系統比較 - 41 -
6-1-1 Extended YaleB資料庫 - 41 -
6-1-2 LFW資料庫 - 45 -
6-2 不同群聚中心個數的比較 - 46 -
6-3 分類器效能比較 - 47 -
6-4 稀疏性(Sparseness)比較 - 49 -
6-5 遞增字典(Incremental Dictionary) - 53 -
6-5-1 遞增字典殘餘值比較 - 53 -
6-5-2 遞增字典與隨機字典比較 - 54 -
6-5-3 遞增字典收斂變化 - 55 -
6-6 唇語辨識 - 56 -
第7章 結論與未來 - 57 -
參考文獻 - 58 -
附錄一 Extended YaleB資料庫 - 63 -
附錄二 LFW資料庫 - 65 -

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