Intelligent Student Attendance Monitoring System Using Face Recognition
Mr. Rahul Ashok Dhone Mr .Soham Gopal Desai, , Ms. Prachi Sachin Dhotre, Mrs. Patil Supriya Jaydeep
Rahul Ashok Dhone ;computer engineering; MARATHWADA MITRA MANDAL’S POLYTECHNIC; rahul_230329@mmpolytechnic.com
Soham Gopal Desai ;computer engineering; MARATHWADA MITRA MANDAL’S POLYTECHNIC; soham_230326@mmpolytechnic.com
Prachi Sachin Dhotre computer engineering; MARATHWADA MITRA MANDAL’S POLYTECHNIC; prachi_230330@mmpolytechnic.com
Mrs. Patil Supriya Jaydeep; computer engineering; MARATHWADA MITRA MANDAL’S POLYTECHNIC; patilsj@mmpolytechnic.com
Abstract— In today’s digital age, a face recognition system plays an important role in almost every field. Face recognition is one of the most commonly used biometric technologies. It can be applied for security, verification, identification, and offers many other benefits. Although its accuracy is comparatively lower than iris and fingerprint recognition, it is widely adopted because it is contactless and non-intrusive. Moreover, face recognition can be effectively utilized for recording attendance in schools, colleges, offices, and other institutions. This system is designed to develop a classroom attendance solution based on face recognition, as traditional manual attendance methods are time-consuming and difficult to manage. There is also a possibility of proxy attendance in manual systems, which increases the necessity for an automated solution. The proposed system operates in four stages: database generation, face detection, face identification, and attendance updating. The database is prepared using students’ images collected from the class. Face detection and identification are carried out using the Haar-Cascade classifier and the Local Binary Pattern Histogram (LBPH) algorithm, respectively. Faces are identified from real-time video streaming in the classroom. At the end of the session, the attendance report is sent to the respective faculty member via email.
Keywords— Automated Face; Identification; Image-Based Face Detection,;Haar Feature-Based Cascade Classifier;Local Binary Pattern (LBP);Automated Attendance System
