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Invariant Range Image Multi - Pose Face Recognition Using Fuzzy Ant Algorithm And Membership Matching Score

机译:不变范围图像使用模糊蚂蚁算法和成员匹配得分多姿态面识别

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In this paper, we present a swarm intelligence based algorithm/or data clustering. Fuzzy c-Means is used on the clusters formed by the ant for speeding up processing time. This approach is developed for implementation the invariant range image multi-pose face recognition system. This face recognition system is created to function covering pose variation region ±24 degrees up/down and left/right (UDLR) from initial pose. RIFD used in this face recognition is based on 3-D Graphics database. For this advantage, we could solve scale, center and pose error problem by using geometric transform RIFD obtained from range image sensors will be used for operation by reducing data size. RIFD will be transformed by the gradient transform into significant feature and matching by using membership matching score. The proposed method was tested by using facial range images from 130 persons with normal facial expressions. The recognition rate has to be better than MMS and k-means.
机译:在本文中,我们介绍了一种基于群体的智能算法/或数据群集。模糊C型方法用于由ANT形成的簇,用于加速处理时间。这种方法是开发的,用于实现不变范围图像多姿态面识别系统。创建该面部识别系统以从初始姿势覆盖覆盖覆盖姿势变化区域±24度和左/右/右(UDLR)。在此面部识别中使用的rifd基于3-D图形数据库。为此优势,我们可以通过使用从范围图像传感器获得的几何变换rifd来解决比例,中心和姿势误差问题,将通过减少数据大小来用于操作。 rifd将通过梯度转换转换为有效的特征和匹配,通过使用会员匹配分数。通过使用具有正常面部表情的130人的面部范围图像来测试所提出的方法。识别率必须优于MMS和K-Means。

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