Parallel Computer For Face Recognition Using Artificial Intelligence

B. Balachandran, Kazi Farzana Saad, Ketu Patel, N. Mekhiel
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Abstract

We implemented a facial recognition application with AI. We used the VGGFace model for our neural net to identify faces. The application includes training and recognizing. The training part is to add new faces to our system, while the recognizing part is to determine the identity of a face. The application runs on multiple cores and able to scale with different numbers of cores. The implementation for parallelism uses tensorflow. For performance measurements, we used the Task Manager application found in Windows with special option known as ‘affinity to choose the number of cores to run the application. The results show that our system scales in performance with number of processors up to twelve.
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基于人工智能的人脸识别并行计算机
我们用人工智能实现了一个面部识别应用程序。我们在神经网络中使用了VGGFace模型来识别人脸。该应用程序包括培训和识别。训练部分是将新面孔添加到我们的系统中,识别部分是确定人脸的身份。该应用程序在多个核心上运行,并能够根据不同的核心数量进行扩展。并行性的实现使用tensorflow。为了进行性能测量,我们使用了Windows中的任务管理器应用程序,该应用程序带有称为“affinity”的特殊选项来选择运行应用程序的内核数量。结果表明,当处理器数量达到12个时,我们的系统性能可以扩展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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