For eye gaze estimation and eye tracking, localizing eye center is a crucial requirement. This task is challenging work because of the significant variability of eye appearance in illumination, shape, color and viewing angles. In this paper, we improve the performance of means of gradient method in low resolution images, which could locate the eye center more accurately. The proposed method applies Supervised Descent Method (SDM), which has remarkable achievement in the field of face align-ment, to improve the traditional means of gradient method in localizing eye center. We extensively evaluate our method on BioID database which is very challenging and realistic for eye center localization. Moreover, we have compared our method with existing state of the art methods and the results of the experiment confirm that the proposed method is an attractive alternative for eye center localization.
|Title of host publication||2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/DataCom/CyberSciTech)|
|Number of pages||6|
|Publication status||Published - 29 Oct 2018|
|Event||16th IEEE International Conference on Pervasive Intelligence - |
Duration: 12 Aug 2018 → 15 Aug 2018
|Conference||16th IEEE International Conference on Pervasive Intelligence|
|Period||12/08/18 → 15/08/18|