首页> 外文会议>Soft Computing and Pattern Recognition, 2009. SOCPAR '09 >Face Sketch Multiple Features Detection Using Simultaneously Shape and Landmark Movement
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Face Sketch Multiple Features Detection Using Simultaneously Shape and Landmark Movement

机译:同时使用形状和地标运动进行人脸素描多特征检测

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Nowadays, retrieving a person identity using a photograph from the face image database is a crucial job especially in police investigations. Unfortunately in many cases, the photo image of a suspect is not available. Only a face sketch drawing based on the recollection of an eyewitness is available. Usually, there are two kind of face sketches employed in police investigations i.e. halftone face sketches. In this paper, we propose a modified line gradient method called Maximum Line Gradient Method to detect multiple features from halftone face sketches by using simultaneously moving shapes and landmarks. Our proposed method is divided into four stages: training, create image gradient, shape initialization, and multiple features detection processes. The last stage is started by searching the maximum line gradient value between two landmarks. Thus, by using the Similarity Transformation Equation, the set of landmarks (shape) will be simultaneously moved. The position of new landmark is enhanced by using simultaneously landmark movements on each shape. In the experiment, we employ 50 halftone face sketches which being examined by using 7 features with 38 landmarks. Our propose method demonstrates that the detection accuracy is 92.16%.
机译:如今,尤其是在警察调查中,使用来自面部图像数据库的照片来检索个人身份是一项至关重要的工作。不幸的是,在许多情况下,犯罪嫌疑人的照片图像不可用。仅提供基于目击者回忆的面部素描图。通常,在警察调查中使用两种面部素描,即半色调面部素描。在本文中,我们提出了一种改进的线梯度方法,称为“最大线梯度法”,可以通过同时移动形状和界标来检测半色调人脸草图中的多个特征。我们提出的方法分为四个阶段:训练,创建图像渐变,形状初始化和多特征检测过程。最后一步是通过搜索两个界标之间的最大线梯度值开始的。因此,通过使用相似度转换方程式,地标(形状)集将同时移动。通过在每个形状上同时使用地标运动来增强新地标的位置。在实验中,我们采用了50个半色调人脸素描,这些素描通过使用具有38个界标的7个特征进行检查。我们提出的方法表明检测精度为92.16%。

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