Classroom Attendance Using Face Detection and Raspberry Pi

International Journal of Research and Scientific Innovation (IJRSI) | Volume VI, Issue V, May 2019 | ISSN 2321–2705

Classroom Attendance Using Face Detection and Raspberry Pi

Santhosh S¹, Dimple², Pranathi Amin³

¹Assistant Professor, Department of CSE, Srinivas School of Engineering, Mukka, Karnataka, India
^2,3Department of CSE, Srinivas School of Engineering, Mukka, Karnataka, India

Abstract: In this paper we propose an attendance management system. This system is based on face detection and recognition. Initially when staffs stand in front of the camera, it detects and recognizes that staff’s face and generates attendance to that particular staff. The system architecture and algorithms used in each stage are described in this paper. When compared to traditional attendance marking this system saves the time and also helps in maintaining staff’s attendance.

Keywords-Face Recognition, Face Detection, Image capture, Feature Extraction, Feature classification.

I. INTRODUCTION

Maintaining the attendance is very important in all the institutes for checking the performance of employees. Every institute has its own method in this regard. Some are taking attendance manually using the old paper or file based approach and some have adopted methods of automatic attendance using some biometric techniques. But in these methods employees have to wait for long time in making a queue at time they enter the office. Many biometric systems are available but the key authentications are same in all the techniques. Face detection and recognition are important application of Image processing owing to its use in many fields. Identification of individuals in an organization for the purpose of attendance is one such application of face detection and recognition. The prevalent techniques and methodologies for detecting and recognizing face fail to overcome issues such as scaling, pose, illumination, variations, rotation, and occlusions. The proposed system aims to overcome the pitfalls of the existing systems and provides features such as detection of faces, extraction of the features, detection of extracted features, recognition of face and analysis of staffs’ attendance. Faces are recognized using Euclidean distance and k-nearest neighbour algorithms.
The system is tested for various cases. We consider a specific area such for marking attendance, for the purpose of testing the accuracy of the system. The metric considered is the percentage of the recognized faces per total number of tested faces of the same person.

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