3.pdf - AUTOMATIC STUDENT ATTENDANCE SYSTEM USING FACE RECOGNITION PROJECT REFERENCE NO:39S_BE_1465 COLLEGE BRANCH GUIDE STUDENTS M S RAMAIAH INSTITUTE

3.pdf - AUTOMATIC STUDENT ATTENDANCE SYSTEM USING FACE...

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AUTOMATIC STUDENT ATTENDANCE SYSTEM USING FACE RECOGNITION PROJECT REFERENCE NO.:39S_BE_1465 COLLEGE : M. S. RAMAIAH INSTITUTE OF TECHNOLOGY, BENGALURU BRANCH : DEPARTMENT OF INFORMATION SCIENCE AND ENGINEERING GUIDE : DR. MEGHA. P. ARAKERI STUDENTS : MR. CHAITANYA P MR. SMITHA BHAT MS. SNEHA R MS. SWATI K.S KEYWORDS:Face detection, Feature Extraction, Face Recognition, LBP INTRODUCTION: Face recognition is an 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 recognition. Maintenance and monitoring of attendance records plays a vital role in the analysis of performance of any organization. The purpose of developing attendance management system is to computerize the traditional way of taking attendance. Automated Attendance Management System performs the daily activities of attendance marking and analysis with reduced human intervention. 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, and analysis of students' attendance. The system integrates techniques such as image contrasts, integral images, color features and cascading classifier for feature detection. The system provides an increased accuracy due to use of a large number of features (Shape, Colour, LBP, wavelet, Auto-Correlation) of the face. Faces are recognized using Euclidean distance and k-nearest neighbor algorithms. Better accuracy is attained in results as the system takes into account the changes that occur in the face over the period of time and employs suitable learning algorithms.
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  • Fall '19
  • Faces, Attendance Management System

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