In every organization attendance is mandatory requirement. Daily maintaining the attendance record is a challenging and time-consuming work. Numerous automated techniques are available for the same like face recognition, biometrics, RFID, eye detection, speech recognition, and many others. But Face recognition provides an accurate approach that eliminates uncertainties like fraudulent attendance, excessive expense, and time consumption because it is well known that a person's face serves as their primary form of identification. The performance of automatic face recognition technology has dramatically increased in recent years. These technologies are now often employed for business and security purposes. An automated system for tracking student attendance in a college using real-time human facial recognition is installed. Consequently, real-time face recognition for smart attendance is a practical solution that deals with managing students' daily activities. Since real-time background subtraction from an image is still a challenge, the task is exceedingly challenging. Open CV library, which is straightforward and quick, is used to quickly and accurately identify the faces found when detecting real-time human faces. The student’s attendance is noted using the matched face. The model automatically updates the student’s attendance data. Manually recording attendance in logbooks becomes time-consuming and complex. In order to handle student attendance records, they created a useful module that includes facial recognition. The module includes the student’s faces. Since enrolling faces is a one-time activity, a mechanism is needed. Student roll number, which will be unique for every student, can serve as student ID. Each student's presence will be updated in a database. Attendance monitoring system using real time face recognition technique increases the accuracy and it consumes less time than any other methods. The implemented system is based on OpenCV library and HAAR-cascade algorithm. They selected these because they have the best accuracy among all and they’re very quick at evaluating HAAR-like features due to the use of integral images. Also, the implemented system used Raspberry Pi device in the project. After tracking the faces, it will exhibit name of student and roll number. All these information stored in attendance sheet automatically updates along with date and time.

Face recognition HAAR cascade Open CV Raspberry Pi RFID Smart attendance system.