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Automated Smart Attendance System Using Face Recognition

机译:自动智能考勤系统使用面部识别

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In the human body, the face is the most crucial factor in identifying each person as it contains many vital details. There are different prevailing methods to capture person's presence like biometrics to take attendance which is a time-consuming process. This paper develops a model to classify each character's face from a captured image using a collection of rules i.e., LBP algorithm to record the student attendance. LBP (Local Binary Pattern) is one among the methods and is popular as well as effective technique used for the image representation and classification and it was chosen for its robustness to pose and illumination shifts. The proposed ASAS (Automated Smart Attendance System) will capture the image and will be compared to the image stored in the database. The database is updated upon the enrolment of the student using an automation process that also includes name and rolls number. ASAS marks individual attendance, if the captured image matches the image in the database i.e., if both images are identical. The proposed algorithm reduces effort and captures day-to-day actions of managing each student and also makes it simple to mark the presence.
机译:在人体中,面部是最重要的因素,因为它包含许多重要细节。有不同的现行方法来捕捉人的存在,如生物识别学,以考虑这是一种耗时的过程。本文使用规则的集合,开发了一种模型,用于将每个角色的脸部分类为从捕获的图像中捕获的图像。,LBP算法记录学生考勤。 LBP(局部二进制模式)是方法中的一种,并且是流行的以及用于图像表示和分类的有效技术,并且选择其鲁棒性以姿势和照明偏移。提出的ASAS(自动智能考勤系统)将捕获图像,并将与存储在数据库中的图像进行比较。使用自动化进程在学生注册时更新数据库,该过程还包括名称和卷编号。 ASAS标志着个人出勤,如果捕获的图像与数据库中的图像匹配,则如果两个图像都相同。所提出的算法减少了努力,捕获管理每个学生的日常行动,并使标记存在简单。

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