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A Hybrid Algorithm of Eyes Localization in Color Facial Region

机译:彩色面部区域人眼定位的混合算法

摘要

In this paper, a hybrid algorithm for precise eyes localization in color facial region is presented In practice, most eye detection suffer from the influence of illumination and face pose. Multiple techniques are integrated in this algorithm to overcome these limitations, such as color space mapping, illumination correction, mouth detecion, dynamic threshold and so on. The scheme consists of two stage: eye region extraction and fine eye loaction. Firstly, with aids of clustering information of eyes in color image and gray-level image, we highlight possible eye regions. Through binarization and morphological operation, we can reserve major blocks and remove most of tiny blocks.For accurate extraction to the best, the threshold of binarization is adjusted appropriately according to heuristic rules test feedback. Heuristic rules are established in terms of priori knowedge about geometrical relationship among facial components. Thereby, we can filter out most of non-eye candidates and allow more possible candidates to be selected as eyes. In the stage of fine eye location, we employ twice extraction to find eye center via projection function and verify validation of loaction by similarty matching finally. With regard to variable lighting, we have adopted an effective local illumination correction to reduce interference.some other experiments are carried out to investigate method capability on variations of pose, facial expressions, partial occlusions and lighting conditions on face images. Experiment results indicate our algorithm can achieves promising performance under certain illumination and various face poses conditions
机译:在本文中,提出了一种用于在彩色面部区域中精确定​​位眼睛的混合算法。在实践中,大多数眼睛检测都受到照明和面部姿势的影响。该算法中集成了多种技术来克服这些限制,例如色彩空间映射,照明校正,嘴部检测,动态阈值等。该方案包括两个阶段:眼睛区域提取和良好的眼球运动。首先,借助在彩色图像和灰度图像中对眼睛信息进行聚类,我们突出了可能的眼睛区域。通过二值化和形态学运算,我们可以保留主要块并删除大部分细小块。为了最大程度地准确提取,根据启发式规则测试反馈对二值化的阈值进行了适当调整。启发式规则是根据有关面部组件之间的几何关系的先验知识而建立的。因此,我们可以过滤掉大多数非眼睛候选者,并允许选择更多可能的候选者作为眼睛。在良好的眼睛定位阶段,我们使用两次提取通过投影函数找到眼睛中心,最后通过相似度匹配来验证运动的有效性。关于可变照明,我们采用了有效的局部照明校正以减少干扰。进行了一些其他实验来研究面部图像的姿势,面部表情,部分遮挡和照明条件变化的方法能力。实验结果表明,我们的算法在一定光照和各种面部姿势条件下都能取得令人满意的性能。

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