Gaze-based interaction technology has been prompted in response to the problem of interaction obstacles of some people caused by the limitations of keyboard and mouse control in the current human-computer interaction. The key step in the vision tracking technology, the pupil, has the problem of low accuracy of pupil center positioning due to the occlusion of the eyelid in the image and the reflection of part of the light, which seriously affects the effect of the human-computer interaction technology based on vision tracking. Therefore, this paper proposes a pupil localization algorithm based on dual edges and occlusion compensation. Firstly, preprocessing such as Gaussian blur is applied to the eye image. Then, histogram-based thresholding is used to binarize the image, obtaining binary image edges and eye image edges. Pupil contour extraction is performed based on dual edges, followed by ellipse fitting using the least squares method to obtain pupil information. Taking into account the problem of poor pupil localization under occlusion, this paper also presents a method to acquire effective edge information and compensate contour points to enhance the accuracy of localization. The experimental results demonstrate that the method proposed in this paper achieves an accuracy of 70.85% on the public dataset LPW, which is 2% higher than the LVCF method. Furthermore, it can effectively locate the pupils under various occlusion conditions.