基于改进的SIFT算子和SVM分类器的瞳孔中心定位

Accurate pupil center location with SIFT descriptor and SVM classifier

瞳孔定位的精确度很大程度取决于图片质量,但实际应用中通常要在低质量图片下进行瞳孔定位。我们的目标是在图片质量不佳的情况下进行精确的瞳孔中心定位。对于这个目标,本文提出一种基于改进SIFT特征和SVM分类器的瞳孔中心初始定位方法,并通过一个大小可变的修正矩形框得到最终瞳孔中心位置。实验结果表明,相比于其他国内外先进方法,本文的方法可以在低质量(光照不均、表情变化等)图片上拥有更高的瞳孔定位精度,定位结果在瞳孔区域内的精度为87.32%。

The accuracy of gaze tracking largely depends on the quality of images, but additional constraints and large amount of calculation make gaze tracking impractical on high-resolution images. Our aim is to get the accurate localization of pupil center on low-resolution image. To this aim, we proposed a simple but effective method which can accurately locate pupil center in real time. The method first gets initial eye center based on improved SIFT descriptor and SVM classifier, and then gets final position of the pupil center through a size variable correction rectangular block. In this paper, comparing with the reported state-of-the-art methods, the experimental results demonstrate that our system can achieve a more accurate result on low-resolution images, the precision of positioning result in the pupil area was 87.32%.