近年來，視線偵測在計算機視覺領域中越來越受歡迎，並在許多應用中被廣泛使用。目前許多方法已經被開發來偵測人們注視的方向。然而，現有的方法往往都是昂貴的，不方便的或具侵入性的。因此本論文提出了一種非侵入式方法僅使用單台消費型深度相機來執行視線偵測，並且能夠在不同照明條件和操作距離下通過使用深度資訊來估計使用者的注視方向。首先，我們利用下巴的輪廓來找到人臉的位置。接著，我們使用人臉的幾何關係來定位出眼睛位置作為感興趣區域(ROI)以降低計算複雜度。接下來，我們利用鼻子和眼睛之間的深度值差異來定位鼻樑，然後利用從眼睛到鼻樑的平均距離來定位出眼睛中心，而在本方法眼睛中心被定義為參考點。之後，我們加強了感興趣區域(ROI)的像素強度，以獲得更明顯的暗瞳特徵來偵測瞳孔的位置。最後，我們通過分析瞳孔和參考點之間的相對位置來獲得注視方向。在通過使用輪廓和人臉的幾何關係，本論文可以在沒有任何顏色資訊的情況下估計視線方向。我們也在三種環境亮度和三個測試距離下針對五位不同的用戶進行了系統驗證。實驗結果表明，本系統的平均準確度達到了80.1%，然而這很接近現有利用RGB相機的視線偵測方法。

Over the years, gaze estimations have become more and more popular in the computer vision processing and been widely used in many applications. Many methods had been developed to solve the problem of determining where people are looking at. However, current gaze estimation methods tend to be expensive, inconvenient, and invasive. This thesis presents a non-invasive approach to estimate the user’s gaze direction based on a single consumer depth camera. The proposed method is able to estimate gaze directions under various lighting conditions and operating distances by using depth information. First, we use the contour of the chin to match the location of the head. Then, we employ the facial geometric relationship to locate the regions of eyes as the ROI for cutting down the computational complexity. Next, we utilize the depth value differences between the nose and eyes to locate the bridge of the nose, and then utilize the average distance from the eye center to the bridge of the nose to locate eye centers, which are defined as reference points in the proposed method. After that, we increase the intensity of ROI to get more obvious features of the dark effects to estimate the location of pupils. Finally, we get the gaze directions by analyzing the relative position be-tween pupils and reference points. By using contours and geometric relationship of head, the proposed method can estimate the gaze direction without any color information. The performance of the proposed system was verified for five different users in three luminance levels and three testing distances. Experimental results show that the proposed method achieves the average accuracy of 80.1 %, where is close to the existing RGB based method.

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