Artificial Attendance System with Two-Way Verification using QR Code Scanning and Face Recognition with Eye Blink Detection

Ghanbahadur, Gaurav Balu, C.Kalpana
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Abstract

: The usage of biometric technology has grown in popularity recently across several industries, including attendance management systems. Utilising biometric technology has been shown to be a more dependable and secure technique of managing attendance. This study suggests an artificial attendance system that uses face recognition with eye blink detection, QR code scanning, and two-way verification. The objective of this system is to enhance the security, accuracy, and reliability of attendance management in various organisations. The suggested system manages attendance via two-way verification. The system first reads a unique QR code that is produced for each user. Second, the system uses face recognition with eye blink detection to confirm the user's identification. The presence and identity of the user are confirmed by the two-way verification. The system also keeps track of the user's attendance's date, time, and place. Deep learning techniques are the foundation of the facial recognition system employed in this study. The system extracts facial features from the user's face using a convolution neural network (CNN). For precise person recognition, the CNN is trained on a big collection of facial photos. Deep learning algorithms are also the foundation of the eye blink detection system. To recognise eye blinks, the system makes use of a long short-term memory (LSTM) neural network. A dataset of facial photos with labelled eye blink data is used to train the LSTM. The Open CV library and Python programming language were used to create the suggested system. A collection of 1000 facial photos with labelled eye blink data was used to test the system. Eye blink detection accuracy for the system was 95% and facial recognition accuracy was 98%. To increase the precision and dependability of attendance management, the proposed system can be implemented in a variety of organisations, such as businesses, schools, and universities. The system may also aid in lowering the time and effort needed for managing attendance and enhancing the security of attendance records
基于二维码扫描和眨眼检测的人脸识别双向验证人工考勤系统
最近,生物识别技术在包括考勤管理系统在内的多个行业中越来越受欢迎。利用生物识别技术已被证明是一种更可靠、更安全的考勤管理技术。本研究提出了一种人工考勤系统,该系统使用了带有眨眼检测的人脸识别、二维码扫描和双向验证。本系统的目的是提高各机构出勤管理的安全性、准确性和可靠性。建议的系统通过双向验证来管理考勤。该系统首先读取为每个用户生成的唯一QR码。其次,系统使用人脸识别和眨眼检测来确认用户的身份。通过双向验证来确认用户的存在和身份。该系统还可以跟踪用户的出勤日期、时间和地点。深度学习技术是本研究所采用的人脸识别系统的基础。该系统使用卷积神经网络(CNN)从用户的面部提取面部特征。为了精确地识别人物,CNN接受了大量面部照片的训练。深度学习算法也是眨眼检测系统的基础。为了识别眨眼,该系统利用了一个长短期记忆(LSTM)神经网络。使用带有标记眨眼数据的面部照片数据集来训练LSTM。使用Open CV库和Python编程语言创建建议的系统。使用1000张带有标记眨眼数据的面部照片来测试该系统。该系统的眨眼检测准确率为95%,面部识别准确率为98%。为了提高考勤管理的准确性和可靠性,所提出的系统可以在各种组织中实施,例如企业,学校和大学。该系统还可以帮助减少管理考勤所需的时间和精力,并提高考勤记录的安全性
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