Face recognition based attendance management system by using machine learning

Salman Baig, Kasuni Geetadhari, Mohd Atif Noor, Amarkant Sonkar
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引用次数: 1

Abstract

Attendance is an important need of every organization. Maintaining daily attendance register is a tough and time consuming task. There are many alternative methods for marking the attendence like Biometric, RFID, eye detection, voice recognition, and many more. But Facial Recognition is an efficient and smart method for marking attendance. As it is known that primary identification for any human is its face, face recognition provides an accurate system which deals with the ambiguities like proxy in attendance, high cost, and time consumption. This system uses face recognizer library for facial recognition and storing attendance. It has a camera that takes an input image, an algorithm to detect a face from the input image, encode it and recognize the face and mark the attendance in a spreadsheet. The system camera of an android phone/laptop clicks the image and sends it to the server where faces are recognized from the database and attendance is calculated on basis of it. The aim of reducing the errors that occur in the older attendance marking system has been achieved by implementing the automated attendance system using deep learning. Face recognition system has been presented using deep learning which shows robustness towards recognition of the users with accuracy of 98.3% and result is converted into a PDF File. Index Terms- Android application, Biometric, Recognition system, Face Recognition, Deep Learning, PDF.
基于人脸识别的考勤管理系统,采用机器学习
出勤是每个组织的重要需求。维护每天的考勤记录是一项艰巨而耗时的任务。有许多其他方法可以标记出勤,如生物识别、RFID、眼睛检测、语音识别等等。但面部识别是一种高效、智能的考勤方法。众所周知,人类的主要身份是面部,人脸识别提供了一个准确的系统,解决了代理出勤、成本高、耗时等模糊性。本系统采用人脸识别库进行人脸识别和考勤存储。它有一个摄像头,可以接收输入图像,一个算法可以从输入图像中检测人脸,对其进行编码和识别,并在电子表格中标记出勤率。android手机/笔记本电脑的系统摄像头点击图像并将其发送到服务器,服务器从数据库中识别人脸并根据其计算出勤率。通过使用深度学习实现自动考勤系统,减少了旧考勤系统中出现的错误。基于深度学习的人脸识别系统显示了对用户识别的鲁棒性,准确率达到98.3%,并将结果转换为PDF文件。索引术语-安卓应用程序,生物识别,识别系统,人脸识别,深度学习,PDF。
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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