Face Recognition at a Distance for a StandAlone Access Control System Although access control based on human face recognition has become popular in consumer applications, it still has several implementation issues before it can realize a standalone access control system. Owing to a lack of computational resources, lightweight and computationally efficient face recognition algorithms are required. The conventional access control systems require significant active cooperation from the users despite its nonaggressive nature. The lightingillumination change is one of the most difficult and challenging problems for humanfacerecognitionbased access control applications. This paper presents the design and implementation of a userfriendly, standalone access control system based on human face recognition at a distance. The local binary pattern (LBP)AdaBoost framework was employed for face and eyes detection, which is fast and invariant to illumination changes. It can detect faces and eyes of varied sizes at a distance. For fast face recognition with a high accuracy, the GaborLBP histogram framework was modified by substituting the Gabor wavelet with Gaussian derivative filters, which reduced the facial feature size by 40% of the GaborLBPbased facial features, and was robust to significant illumination changes and complicated backgrounds. The experiments on benchmark datasets produced face recognition accuracies of 97.27% on an Eface dataset and 99.06% on an XM2VTS dataset, respectively. The system achieved a 91.5% true acceptance rate with a 0.28% false acceptance rate and averaged a 5.26 framessec processing speed on a newly collected face image and video dataset in an indoor office environment.

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 userfriendly interface has been designed to improve the attendance marking process. Image preprocessing 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 preregistration 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%.