基于视觉词袋和基于mlp的bp神经网络的混合计算机人脸识别系统

L. Rao, Coneri Harshitha, C. Z. Basha, Nazia Parveen
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引用次数: 1

摘要

如今,在covid - 19大流行这样的情况下,使用生物识别系统来监控员工的出勤情况非常敏感。原因是covid - 19很容易通过生物识别系统从一个人传播到另一个人。任何组织都有必要维护一个不采集任何员工或学生指纹的考勤监控系统。自动人脸识别系统最好替代生物识别系统。本文提出了一种基于视觉词袋(BOVW)和基于多层感知器(MLP)的反向传播神经网络(BPNN)的人脸自动识别技术。该方法的准确度达到91%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Hybrid Computerized Face Recognition System Using Bag of Visual Words and MLP-Based BPNN
Nowadays in a situation like the Covid19 pandemic it is very sensitive to use biometric systems for attendance monitoring of employees. The reason is covid19 spreads from one person to another easily with a biometric system. It has become necessary for any organization to maintain an attendance monitoring system without taking fingerprints of any employee or a student. The automatic Face recognition system is best to alternate for the biometric system. An advanced automatic face recognition technique is proposed in this paper with the classification technique using Bag of Visual Words (BOVW) and Multi-Layer Perceptron (MLP) based Back Propagation Neural Network (BPNN). An Accuracy of 91% is achieved with the proposed methodology.
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