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Enhanced Face Recognition Method Based On Local Binary Pattern and Principal Component Analysis For Efficient Class Attendance System

In this paper, an enhanced face recognition method is proposed to develop an efficient student attendance system. In the face recognition stage, enhanced local binary pattern (ELBP) is implemented by increasing the radius of the original local binary pattern (LBP) operator and principal component analysis (PCA) is used to extract the features from facial images. The ELBP outperforms the original LBP by suppressing the adverse influence of different illumination conditions and thus results in increased recognition rate. The test recognition rate on the Yale face database of the original LBP is 81.9%, while the ELBP with radius 2 to 5 pixels has achieved face recognition rate of 84.6% to 88.5%. The face images are recognized based on the classification results obtained from the combination of algorithms (PCA and ELBP). A user-friendly interface has been designed to improve the attendance marking process. Image pre-processing is performed to ensure better performance of the system. Lastly, the attendance of the recognized students is marked accordingly and saved in an excel sheet. Students who have not undergone pre-registration can register on the spot. Notification will be generated if a student signs in more than once. The recognition rate for high quality images in the attendance system is 100% whereas for low quality images it is 92.31%.