面部识别考勤系统

Mohammad Afzal l Nezam
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引用次数: 0

摘要

通过从教室照片中识别学生的正面脸部,本研究试图构建一个通用的人脸检测和识别系统,使收集学校考勤的过程自动化。传统考勤管理系统的主要问题是收集数据的准确性。目前使用的自动化技术很多,包括生物识别考勤。然而,这些方法的有效性总是受到扫描设备技术问题的影响。为了提高数据质量和信息的可访问性,本文使用 OpenCV 进行人脸识别,并使用主成分分析技术进行人脸检测。系统中保存用户数据的数据库使用 SQL 开发,而建议的系统则使用 Python 编程语言创建。经过测试,确定新系统是安全的,并通过提供匿名考勤环境确保了学生身份的安全。关键词:(ABS)人脸检测;考勤;机器学习;数据库;主成分分析;CNN;OpenCV 和人脸识别
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Facial Recognition Attendance System
By identifying students' frontal faces from classroom photos, this research attempts to construct a general face detection and identification system that will automate the process of gathering school attendance. The main issue with conventional attendance management systems is the accuracy of the data that is gathered. Numerous automated techniques are in use, including biometric attendance. Nonetheless, the effectiveness of these methods is always impacted by scanning equipment technical issues. In order to enhance data quality and information accessibility for authorised parties, this article uses OpenCV for face recognition and principal component analysis techniques for face detection. The database that holds user data in the system was developed using SQL, while the Python programming language was utilised to create the suggested system. After testing, it was determined that the new system is safe and secures students' identities by providing an anonymous attendance environment. Keywords: (ABS) Face Detection; Attendance; Machine Learning; Database; Principal component analysis;CNN; OpenCV and Face Recognition
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