基于人工神经网络的android界面人脸识别系统

Kevin Alamsyah Yuwono, Irma Safitri, Iwan Iwut Tritoasmoro
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引用次数: 1

摘要

人脸识别系统是当今的一个关键问题。本研究利用Gabor滤波和人工神经网络(ANN)方法构建了一个基于android的实时人脸识别系统。系统可以正常运行。测试结果表明,对于场景1的测试,隐藏层4和隐藏层5的准确率最高,达到90%。第2层最小的计算时间为0.46872秒,第5层最大的计算时间为0.63778秒。而场景2的测试结果显示准确率最低的是trainrp训练函数,准确率为76%,而准确率最高的是traincgp训练函数,准确率为94%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Artificial Neural Networks Android-Based Interface Facial Recognition Systems
Face recognition system is a crucial issue these days. This research builds an Android-based facial recognition system in real time using the Gabor filter and artificial neural network (ANN) methods. The system can be implemented properly. The test results show that for testing in scenario 1, the largest accuracy is 90% in hidden layer 4 and 5. The smallest computation time is 0.46872 seconds for layer 2 and the biggest time is 0.63778 seconds for hidden layer 5. While the test results for scenario 2 shows the lowest accuracy is the trainrp training function for 76%, while the highest accuracy of 94% is in the traincgp training function.
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