人脸识别系统

Prerit Goel, Ritin Behl, Pranjul Aggarwal, Manish Srivastava, Sanyam Gupta
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引用次数: 1

摘要

人脸确认(FR)是一种通过人脸照片来识别人的方法,在统计学、信息安全领域有各种可负担得起的应用;掌握控制、法律需求、悟性和观察框架。卷积神经网络(Connets)作为深度系统的一种形式,在人脸识别(FR)方面取得了无可争议的成果。对于正在进行的框架,在使用连接之前应该执行一些预处理方法,如检查。因为它当时同样可以完成镜头(所有组成部分)的平方度量作为对网络的贡献和其中的每一个建议(突出确定、体现提取、准备)的平方度量由系统执行。这可能是死刑在一个较为先进和繁琐的过程中只进行一次量刑的基本原理。在开始阶段,连接线的平方测量精度也非常高,这样它们就有了行进的道路。本文提出了另一种利用深度神经系统(另一种相当深刻的系统)进行面部识别的技术。在此方法中,不再给出粗糙的成分值作为数据,而是简单地给出分离的面部亮点平方度量。这就降低了“然而”的多样性,给出了“九十七”的精确性。耶鲁面对数据集的5%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Face Identification System
Face Affirmation (FR), the methodology toward recognizing people with the help of their facial pictures, has various affordable applications within the zone of statistics, information security; get to control, law demand, savvy cards and observation framework. Convolutional Neural Networks (Connets), a form of profound systems has been incontestable to be fruitful for Face Affirmation (FR). For in progress frameworks, some pre-process methods like examining ought to be done before utilizing to Connets. Be that because it could, at that time likewise complete footage (all the constituent esteems) square measure passed as contribution to Connets and each one amongst the suggests that (highlight determination, embody extraction, preparing) square measure performed by the system. this can be the rationale that death penalty Connets square measure once during a whereas advanced and tedious. Connets square measure at the start stage and also the exactnesses got square measure extraordinarily high, in order that they have way to travel. The paper proposes another technique for utilizing a profound neural system (another quite profound system) for facial acknowledgment. During this methodology, instead of giving crude constituent esteems as data, simply the separated facial highlights square measure given. This brings down the multifarious nature of whereas giving the exactness of ninety seven.05% on Yale faces dataset.
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