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Identifying Resemblance in Local Plastic Surgical Faces Using Near Sets for Face Recognition

机译:使用近集进行面部识别来识别局部整形外科面部的相似性

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In recent years, much advancement have been made in face recognition techniques which leads to the popularity of plastic surgery procedures. Pose, illumination and expressions are some of the problems that have been already recognized and studied in the domain of face recognition. In this paper, we have proposed an approach based on near set theory to develop a classifier for facial images that have previously undergone some feature modifications through plastic surgery. Our work concerns only geometrically obtained feature values and their approximation using near sets. Near set theory provides a method to establish resemblance between objects contained in a disjoint set, that is, it provides a formal basis for observation, comparison and classification of the objects. The experimental results indicate the performance and accuracy of the plastic surgery based face recognition.
机译:近年来,已经采用了面部识别技术进行了大量进步,从而导致整形手术程序的普及。姿势,照明和表达是在人脸识别领域已经认可和研究的一些问题。在本文中,我们提出了一种基于近集理论的方法,为通过整形手术进行先前经过一些特征修饰的面部图像的分类器。我们的工作仅涉及几何上获得的特征值及其近似使用近套。近集理论提供了一种在不相交集合中包含的对象之间建立相似性的方法,即它为物体的观察,比较和分类提供了形式的基础。实验结果表明了基于整形手术的人脸识别的性能和准确性。

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