人脸彩色图像的眼睛检测算法

J. Nasiri, Sara Khanchi, H. Pourreza
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引用次数: 29

摘要

在人脸检测或识别等许多应用中,眼睛检测是一个重要的阶段。此外,其广泛的应用作为一个重要的组成部分,使其成为一项重要的工作。使用颜色特征是检测眼睛的有效方法。我们使用特殊的颜色空间,YCbCr,它的组成部分给我们关于眼睛的有价值的信息。我们根据它的组成部分制作两个地图,并将它们合并得到最终的地图。在最终的地图上生成候选地图。我们对候选人施加额外的相位以确定合适的眼睛。额外阶段包括灵活阈值和几何测试。灵活的阈值可以更仔细地生成候选对象,几何测试允许选择合适的候选对象作为眼睛。在CVI和伊朗数据库上的仿真结果表明,该阶段的检测正确率提高了12.4%左右,平均成功率达到98.5%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Eye Detection Algorithm on Facial Color Images
In many application suck as face detection or recognition a major phase would be eye detection. In addition, its wide use as a part of serious applications, made it an important task should be worked on. Using color characteristics is a useful way to detect eyes. We use special color space, YCbCr which its components give us worthwhile information about eyes. We make two maps according to its components and merge them to obtain a final map. Candidates are generated on this final map. We apply an extra phase on candidates to determine suitable eye pair. The extra phase consists of flexible thresholding and geometrical tests. Flexible thresholding makes generating candidates more carefully and geometrical tests allow proper candidates to be selected as eyes. Simulation results on CVI and Iranian Databases showed this phase improved the correct detection rate by about 12.4% and reach 98.5% success rate on the average.
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