基于FaceNet和支持向量机的大学课堂考勤系统

Thida Nyein, Aung Nway Oo
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引用次数: 16

摘要

目前,人脸识别系统已成为研究领域的热点。人脸识别还应用于考勤管理系统、人员跟踪系统、门禁系统等诸多应用领域。对于多人脸识别来说,由于在一帧图像中不易检测出多张人脸,且分辨率较差的人脸也难以识别,因此在检测和识别方面仍然存在许多挑战。因此,本文的主要目标是将FaceNet与支持向量机(SVM)相结合,以获得更好的多人脸识别精度。在该系统中,FaceNet通过每张人脸嵌入128个维度来进行特征提取,并利用提取的FaceNet特征对给定的训练数据进行分类。提出的多人脸识别技术应用于高校课堂考勤系统。实验结果表明,该方法可以很好地实现多人脸识别,准确率达到99.6%。在相同的数据集上,它优于VGG16模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
University Classroom Attendance System Using FaceNet and Support Vector Machine
Nowadays, face recognition system becomes popular in research area. Face recognition is also used in many application areas such as attendance management system, people tracking system, and access control system. For multi-face recognition, it has still many challenges for detection and recognition because it is not easy to detect multiple faces from one frame and it is also difficult to recognize the faces with poor resolution. Therefore, the main objective of this paper is to get a better accuracy for multi-face recognition by using the combination of FaceNet and Support Vector Machine (SVM). In this proposed system, FaceNet is used for feature extraction by embedding 128 dimensions per face and SVM is used to classify the given training data with the extracted feature of FaceNet. University Classroom Attendance System is applied by the proposed multi-face recognition. The Experimental result show that the proposed approach is good enough for multi-face recognition with an accuracy of 99.6%. It is better than VGG16 model on the same data-set.
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