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Automatic system for recognition and identification of human objects

机译:自动识别和识别人体物体的系统

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The reason for this paper is to present short audits the face recognition systems and to concentrate the impact of features pattern examination in recognizing facial elements. In exploring the current methods, we have looked at the execution of Haar Training actualized inside Open Source Computer Vision Library (OpenCV), HOG, PCA, DCT and Keinzle's calculation in face identification. At that point, we have created some exploratory outcomes recommending that the features extraction and matching approach would extricate key facial components of a human face. The outcome shows that our proposed approach is sufficiently effective. FRR and FAR of our approach has less values so it reflect that the proposed approach is productive. Besides, our proposed approach has less Error rate and additionally coordinating rate is over 91%. The association between picture designs pattern and image acknowledge in the structure of the SHIFT Key, order, genetic algorithm (GA), FAR, FRR and error considered.
机译:本文的原因是要对面部识别系统进行简短审核,并集中特征模式检查对面部元素的识别的影响。在探索当前方法时,我们研究了在开源计算机视觉库(OpenCV)中实现的Haar Training的执行,HOG,PCA,DCT和Keinzle在面部识别中的计算。在这一点上,我们已经创建了一些探索性结果,建议特征提取和匹配方法将使人脸的关键面部组件变得更加精致。结果表明,我们提出的方法是足够有效的。我们的方法的FRR和FAR具有较小的价值,因此表明所提出的方法是有效的。此外,我们提出的方法具有较低的错误率,另外协调率超过91%。图片设计模式与图像之间的关联在SHIFT键,顺序,遗传算法(GA),FAR,FRR和所考虑的错误的结构中得到确认。

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