本論文提出一種改良式臉部特徵點擷取技術與具創新性的眼睛辨識系統，並將兩者技術結合實現一套高準確率且快速的身分辨識系統。
在臉部特徵點擷取方面，透過改進隨機森林的分割法則與特徵的擷取，使其能更準確的將樣本分群，使線性迴歸能更精準的估測其偏移量，這些都反映在實驗數據上，透過兩者技術結合與數量龐大的資料庫訓練，使其能抵抗光源、角度等外在因素帶來的影響。
在眼睛辨識方面，透過幾何、紋理與雙眼皮特徵彼此間的搭配組合，並透過支持向量機訓練，使其能更準確的判斷出待測者的身份，使系統具有高準確率及低計算複雜度的表現。且其資料庫具有很高的容忍範圍，包含正常眼睛、眨眼，甚至是閉眼，都擁有很高的辨識準確率。
在實驗結果方面，針對臉部特徵點擷取技術，我們使用LFPW與Helen資料庫分別進行測試，並與前人技術進行比較，從實驗結果可以得知其表現程度都是最好的。而針對眼睛辨識系統，我們使用CMU PIE與Yale人臉資料庫分別進行測試並與前人技術比較，從結果可看出皆有良好的準確率。且由於系統具有高準確的特性，因此可被應用於安全與辨識系統中。

This thesis presents two strategies on ocular recognition system to achieve a high performance accuracy and low computational complexity. The first technique is the modified facial landmarks extraction which is the key aspect on feature extraction for identification system. The second strategy enables an innovative ocular recognition system.
In terms of the facial landmarks extraction, the former random forest is improved with the proposed angle-split tactic to reduce the error rate. On the other hand, the facial landmarks extraction requires an additional process for the suppression of the noise interference. In this thesis, Gaussian blur filter is employed to alleviate the noise effect and to achieve a low error rate.
In the ocular recognition system, the proposed method combines three various features, i.e., geometric, texture, and eye-fold texture, which can describe all bio-invariant properties of an ocular region. The support vector machine is then exploited to train a model to achieve a good recognition performance. As a result, the proposed system achieves a great flexibility in handling open eyes, blinking eyes, and closed eyes.
Experimental results validate the successfulness and effectiveness of the proposed method over two standard image databases, i.e., LFPW and Helen databases. The proposed method reduces the error rate on the facial landmarks extraction stage. The performance of proposed method is also examined and investigated over another two image databases, i.e., CMU and Yale databases, which consist of open-and-blinking eyes scenarios. As documented in the experimental results, the proposed method offers a promising result in terms of recognition rate, and outperforms the former schemes. Thus, the proposed system can be regarded as an effective candidate in the biometric applications requiring real-time processing.

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