以類神經網路為基礎之眼動追蹤系統
摘要
　眼睛是人類接收外界資訊的重要器官，同樣可以傳達資訊如注視的方向，周圍環境的明亮度等等，也是人類能夠用來表達情緒的器官，所以眼動追蹤一直都是熱門的研究課題。 　　近年來有越來越多的眼動追蹤儀推出市面，並且應用更為廣泛，心理學、醫學、教育、虛擬實境等都能看到眼動追蹤的應用。 但目前大部分商用的眼動追蹤儀價格昂貴，並且頭部需要固定才能準確估測注視的方向。 　　本篇論文使用類神經網路結合本論文所提出的 Inner Corner-Pupil Center Vector (ICPCV)特徵，製作出可以讓使用者不用固定頭部而且不需要使用昂貴的硬體就能使用的眼動追蹤系統，並且與使用係數矩陣注視點估測演算法作比較，有更好的表現。

關鍵字： 眼動追蹤、類神經網路、Inner Corner-Pupil Center Vector

A Neural-networks-based Eye-Tracking System
Abstract
　The eye is an important organ that human receives information via from the outside world. It also can convey information such as the direction of gaze, the brightness of the surrounding environment and express emotions. Therefore, eye tracking has always been a popular research topic. 　　In recent years, there are more and more eye trackers produced and on sale. The application of eye tracking is more wide-ranging such as psychology, medicine, education, virtual reality. But most of the eye tracker in the market is very expensive, and the head needs to be fixed in order to accurately estimate the direction of gaze. 　　This study develops an eye tracking system based on neural network and Inner Corner-Pupil Center Vector (ICPCV) feature which defined by ourselves. Make it allows user move his/her head and needn’t expensive hardware. We compared with coefficient matrix gaze estimation algorithms and better than it. Key Words: Eye-tracking, Neural-network, Inner Corner-Pupil Center Vector