本論文貢獻有二: 1)高效能局部穩定特徵萃取與2)改良式指紋特徵擷取技術，並將兩者結合，實現一套即時指紋辨識系統。
在高效能局部穩定特徵中，本論文提出快速二元強健不變特徵(Fast Binary Robust Local Feature, FBRLF)來有效率地萃取出影像之局部不變特徵與描述。首先，利用自適性角點檢測技術來搜尋影像範圍內的穩定特徵角點，其中預估的穩定特徵可以由兩個可調式參數，最低限制(Lower Bound)與最高限制(Upper Bound)來控制，藉此提升特徵角點的穩定性。為了抑制影像中雜訊的干擾，我們利用高斯權重模板來模擬周圍像素對特徵點的影響力，並透過積分影像的概念來降低計算複雜度，同時為了更進一步改善匹配速度，於是提出投票機制查詢表來針對匹配特徵進行加速，實驗結果顯示本論文提出的方法具有良好的不變性特徵與實用價值，因此可以應用於不同的領域，例如圖形識別、生物辨識系統和監控系統。比起前人的局部穩定特徵技術，本論文提出的方法可以同時提高匹配率及達到即時運算。
在指紋辨識系統方面，基於傳統的指紋細節特徵之演算法，往往需要伴隨高複雜度的影像前處理與不穩定之細節特徵萃取，造成指紋辨識系統精準度不佳及高運算成本。針對上述問題本論文提出所提出之基於快速二元強健不變特徵(FBRLF)之指紋辨識演算法來解決傳統指紋辨識技術的辨識率與效率問題。同時，本論文對於微小型指紋感應器提出穩健的指紋採集方式。實驗結果方面，本論文採用實驗室資料庫與FVC公開資料庫個別進行測試，並與前人的方法比較，從結果可以看出不論是系統辨識率或處理效能皆是相當突出的。最後為了將所學回饋於現實生活之中，本論文將所提出之指紋辨識演算法以平板電腦與微光學式感應器設計出一套介面化即時指紋辨識系統。

This thesis presents two techniques for a real-time fingerprint recognition system. The first technique delivers an efficient feature extraction with local invariant capability, namely Fast Binary Robust Local Feature (FBRLF), and the other technique presents the improved strategy for the fingerprint recognition system.
The FBRLF searches the stable features on an image which can be simply modeled using the adaptive Features from Accelerated Segment Test (FAST) corner detection with two user-defined parameters to yield stable features. To overcome the image noise, the Gaussian template is applied, and it is efficiently boosted by the integral image evaluation. In addition, the feature matching is conducted by incorporating the voting mechanism and lookup table method to achieve a high accuracy with low computational complexity. Experimental results demonstrate the superiority of the proposed method, making it suited for various applications such as pattern recognition, biometrics recognition systems, surveillance system, etc. In particular, the proposed method achieves a superior match rating in real-time fashion compared to that of the former competing schemes.
The traditional minutiae-based fingerprint recognition system requires a high computational complexity in the preprocessing stage. Most of them invlove unstable features, leading to a poor fingerprint recognition accuracy and heavy computational burden. The proposed fingerprint recognition system overcomes these issues by utilizing the proposed FBRLF. This system is built under Surface Pro 3, and is equipped with micro-optical fingerprint scanner to construct a real-time fingerprint recognition system. Two fingerprint databases are involved for the performance test of the proposed system. The first database is from manually generated fingerprint images, and the other is the FVC fingerprint standard database. Experimental results clearly demonstrate the superiority of the proposed method compared to the former schemes in terms of fingerprint recognition performance and processing efficiency.

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