| 摘要: | 在本論文中，我們主要探討二個以生物特徵為基礎的圖形識別(biometric-based pattern recognition) 問題 --手寫簽名辨認(handwritten signature verification) 及人臉識別(human face recognition)。就資訊技術角度而言，生物特徵辨識法指的是一種自動化的技術，依據某人生理特性或特徵的度量值與資料庫的記錄比對，進行個人身份比對或驗證的工作。上述針對生理特性或特徵的度量包括人臉、人體味道、虹膜、視網膜、指紋、手掌形、皮膚毛孔、手掌脈紋、手腕脈紋、手寫簽名、按鍵或打字特性及聲紋等。 在處理第一個問題即手寫簽名辨認方面。其中我們主要利用波元理論、過零點、動態時間歸正、及非線性整數規劃等技術來解這個問題。此處所提出的解法可以在同一人所簽的多個不同簽名中，自動分辨出有用且具共同性的特徵，並運用這些特徵以辨認某一特定簽名的真偽。這系統首先執行一個簽名影像封閉輪廓追蹤（tracing）的程式，接著以小波轉換技術將所取得的封閉輪廓曲線資訊分解成不同解析度的訊號。之後再分解出上述不同解析度之曲線中的過零點當做特徵供後續比對之用。此外，我們也設計出一種有效的統計度量方法，以便有系統的自動決定，針對各不同簽名者而言，最穩定且最具區分能力的是那些封閉輪廓及那個頻寬的資訊。基於這些穩定且具區分能力的資訊，我們才能求得較佳的臨界值以增進系統效能。我們深信此處所提出的方法同時適用於線上及離線手寫簽名辨識系統。 本研究中所探討的第二個有關問題是人臉識別，我們主要利用由 Juang 和 Katagiri [11] 所提出的最小分類錯誤法(MCE, minimum classification error method) 。這個方法主要是將傳統的區分分析法與區分法則結合以形成一個新的函數，並用它來當作目標準則，以便後續以數值搜尋演算法求得最佳解。在這部份研究中，我們提出了一個以最小分類錯誤法為基礎的人臉識別系統，在這個系統中，MCE方程式被引用在一個三層式的類神經網路分類器（稱作多層感知機，MLP）中。以上所提出的系統具有許多優點，首先，它不像傳統以機率為基礎的貝氏決策方法一樣，它不需要事先去假設各類別所採用的機率模式。其次，這種分類器即使在訓練樣本很小時，依然有效運作。並且，不論在一般正常情況或是惡劣的環境，以MCE為基礎的系統總是比傳統上以MSE（Minimum Squared Error）為基礎的類神經網路分類系統表現得好。最後，由於這裡所提出的系統同時採用了我們先前研究中所出的一種快速人臉偵測法以協助在一張具有複雜背影的輸入影像中取出所要的人臉（face-only）部份，因此不論是在諸如複雜影像背景、多雜訊、或不良照明等之惡劣環境下，我們所提出的以MCE為基礎的人臉識別系統均可穩定的（robust）運作。由實驗結果可證實我們所提出的方法較優於其他方法。 In this dissertation, two biometric-based pattern recognition problems were studied, i.e., off-line handwritten signature verification and human face recognition. Biometrics, by definition, is the automated technique of measuring a physical characteristic or person trait of an individual and comparing the characteristic or trait to a database for purposes of recognizing or authenticating that individual. Biometrics uses physical characteristics, defined as the things we are, and personal traits, defined as the things we behave, including facial thermographs, chemical composition of body odor, retina and iris, fingerprints, hand geometry, skin pores, wrist/hand veins, handwritten signature, keystrokes or typing, and voiceprint. To deal with the first biometric-based pattern recognition problem, i.e., off-line handwritten signature verification. Wavelet theory, zero-crossing, dynamic time warping, and nonlinear integer programming form the main body of our methodology. The proposed system can automatically identify useful features which consistently exist within different signatures of the same person and, based on these features, verify whether a signature is a forgery or not. The system starts with a closed-contour tracing algorithm. The curvature data of the traced closed contours are decomposed into multiresolutional signals using wavelet transforms. Then the zero-crossings corresponding to the curvature data are extracted as features for matching. Moreover, a statistical measurement is devised to decide systematically which closed contours and their associated frequency data of a writer are most stable of a writer are most stable and discriminating. Based on these data, the optimal threshold value which controls the accuracy of the feature extraction process is calculated. The proposed approach can be applied to both on-line and off-line signature verification systems. The second biometric-based pattern recognition problem we deal with is human face recognition; we applied the minimum classification error (MCE) technique proposed by Juang and Katagiri[11]. In this technique, the classical discriminant analysis methodology is blended with the classification rule in a new functional form and is used as the design objective criterion to be optimized by numerical search algorithm. In our work, the MCE formulation is incorporated into a three-layer neural network classifier called multilayer perceptron (MLP). Unlike the traditional probabilistic-based Bayes decision technique, the proposed approach is not necessary to assume the probability model of each class. Besides, the classifier works well even when the size of a training set is small. Moreover, no matter in normal environment or harsh environment, the MCE-based method is superior to the minimum sum-squared error (MSE) based method which is commonly used in traditional neural network classifier. Finally, by incorporating a fast face detection algorithm into the system to help for extracting the face-only image from a complex background, the MCE-based face recognition system is robust to image acquired from harsh environment. Experimental results confirm that our approach outperforms the previous approaches. |
