近年來生物資訊蓬勃發展，尤其是生物特徵辨識更是熱門的研究領域，特別是在911攻擊事件之後，各國的專家學者更是努力鑽研生物特徵辨識領域，用以發展出一套完整的安全防護系統，防範可能的恐怖攻擊行動。在此情況下，人臉辨識成為主流的研究之一，畢竟人臉是最直覺、最接近我們日常生活的感覺，但也是最難以實現高辨識率的。

使用賈伯(Gabor)濾波器是一種相當好的特徵擷取方式，尤其是用在指紋辨識以及人臉辨識上都有相當不錯的效果。統計學習理論近幾十年來在機器學習領域上是一個相當熱門的研究議題，代表性的類神經網路已經被用於許多應用上，且有不錯的成效。近年來發展出來的支持向量機(SVM)則是有更佳的分類能力，並起避免了類神經網路可能有的過學習效應。目前已有許多文獻證明SVM應用於辨識、分類、探勘等領域皆有相當不錯的表現。

鑒於安全防護的重要，本論文提出了結合Gabor特徵擷取、SVM分類辨識、以及雙門檻值的概念，建構出一個門禁管制系統平台。在SVM分類上，一對一方式在應用於純粹辨識領域時的表現較一對多的方式為優。然而當應用於門禁管制時，由於一對多的方式有其獨特的排外性質，因此相當適合用於門禁管制的應用上。故本論文所建立的平台採用一對多的分類方式，以模擬現實情況中安全防護的情形。

The study in biometrics field has vigorous development these years, especially in feature-based recognition field. Since the September 11 attacks, there are more and more specialists dig into biometrics field to develop a security system to guard against the horror attacks; therefore, the study in face recognition has become one popular method in biometrics researches. However, the face recognition is the not easy to reach high recognition rate even though it is the most intuitional method.

It is a good way to extract features by using Gabor filter, especially in finger print recognition and face recognition. The statistical learning theory is a hot topic these decades. The neural network has been used in many applications and has performed very well. These years, the support vector machine has showed its good classification ability and there are many papers proved that it performs better than neural network in some applications such as biometrics recognition, document classification, and data mining.

In this thesis, I built a MATLAB GUI based security platform. This platform combines Gabor feature extraction, SVM classification, and duo-threshold concepts to simulate the real-world security system. While using the SVM classifiers, I adopted one-against-rest method instead of one-against-one method because the former one has better exclusivity.

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