現行的視線追蹤系統可以應用於許多地方，例如居家看護、滑鼠控制以及線上學習等等。而如果是希望做一般性的使用，也就是希望系統的安裝時間或者使用難易度等等都會容易使用，此時，非侵入式的系統比較被建議，也就是架設一台攝影機在固定地方來追蹤視線而建構出的系統。然而，對於大部分的非侵入式系統，都會要求使用者的頭部保持固定不動，對於漸凍人這類的病人而言，這是行的通的，但是若希望一般人也可以使用，就會變的非常困難，原因是在於大部分的人都沒辦法長時間的保持頭部固定，也因此，會使得系統的正確率下降許多。
因此，為了解決頭移動限制的問題，在本篇論文中，提出了一個有效的解決辦法。此演算法的流程是，在輸入部分為一320*240 大小的眼睛圖，接著取出已
定好的特徵，包括了兩個眼角位置以及用來表示虹膜輪廓的橢圓之中心點、兩軸比例和方向等等，共有八個。接著，把這些特徵值代入在校正過程中已訓練好的類神經網路，而類神經網路的輸出值則是最後所推測的注視點位置。與其他系統方法不同點的地方在於，用來表示虹膜輪廓的是橢圓，而非圓形，這是因為用圓形會失去很多資訊。此外，輸入部分和輸出部分（注視點）之間的對應函式不使用二階或三階的多項式，而是利用了類神經網路來做函式逼近。而特徵的取決，由於也考量到頭移動的因素，因此除了使用眼睛中心點作為特徵外，也取了多個有意義的特徵。最後，實驗結果證明了本研究所提出的演算法是可以用來解決頭移動的限制。

A gaze estimation method using the eye position and the iris contour is proposed in this thesis. In traditional methods, users’ heads are asked to remain still for a long time which is an exhausted and fatigue work. In order to create a comfortable environment, users’ heads are allowed to move freely. In our approach, the eye region is assumed within the view of the camera. As we know, the mapping between the gaze points and the eye region is difficultly to be formulated as a polynomial. The zoomed and clear eye images were grabbed to increase the accuracy of gaze estimation. Therefore, the center of an eye ball and the shape of iris contour should both be considered. First, the eye corners are located manually to estimate the center of eye
ball. Using this information, the pose of a human head is guessed to be a global gaze feature. In addition, the iris center, size, and orientation, called local gaze features, are
calculated and integrated to train a neural network (NN). Instead of a polynomial function approximation, the NN was trained to estimate the mapping between the gaze features and the gaze points. Experiments were conducted and the results demonstrate the effectiveness of the proposed method in gaze estimation. Finally,
conclusions are given and future works are suggested.

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