基于人脸识别的TE学生识别系统的深度学习技术

K. Klinieam, P. Noiying
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引用次数: 0

摘要

在本研究中,我们旨在测量CiRA CORE软件的人脸识别算法的鲁棒性,并使用CiRA CORE软件的深度学习技术识别一组具有人脸识别的TE学生。一次性问题和相似脸问题是CiRA CORE的鲁棒性性能测试。因此,数据集图像的数量和属性被认为是CiRA CORE软件的鲁棒性。识别TE学生实验;三个检测到的人脸是一帧中人脸数量的最大值,这是实验的一个条件。识别TE学生实验结果表明,该系统在戴帽伪装测试中准确率达到90%,在其他伪装问题和识别TE学生测试中准确率达到100%。鲁棒性和识别TE学生实验结果验证了CiRA CORE软件人脸识别算法对TE学生识别系统的有效性和鲁棒性。
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Deep Learning Technique for a Identify TE Student System by Face Recognition
In this study, we aim to measure the face recognition algorithm robustness of CiRA CORE software and identify a group of TE student with face recognition using the deep learning technique from CiRA CORE software. The one-shot problem and similar-looking face problem are robust performance tests of CiRA CORE. Thus, the number and properties images of a dataset are considered as the robustness of the CiRA CORE software. Identify TE student experiment; three detected faces that are the maximum number of faces in one frame is a condition of the experiment. The results of identify TE student experiment demonstrate that the system gains ninety percent accuracy for the camouflage test by wearing a cap and achieves one hundred percent accuracy for other camouflage problems and identify TE student test. The result of robustness and identify TE student experiment confirms face recognition algorithm CiRA CORE software is effective and robust for identify TE student system.
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