The old method of marking attendance involves the lecturer providing an attendance sheet to the students for their signature or the teacher calling out students name individually to mark them present. This old manual method is pretty hectic for teachers and students too. Since, after taking the signed attendance sheet from students, teachers have to manually keep track of every student in the logbook which turned out to a lot of time wastage, missing out students presenteeism or students giving proxies for the absentees due to which logbooks can be easily manipulated or prone to errors also, wastage of pen and paper. To avoid this problem, we have developed a system which will monitor the attendance of students by identifying their faces via their facial features. While developing this system we have used a web Camera to capture multiple live images of students for face recognition, Viola-Jones Algorithm to achieve face detection which uses Haar Cascade classifier, Pre- processing which converts the image in greyscale, LBPH algorithm and deep learning algorithms like CNN (Convolutional neural networks) for feature extraction and last but not the least the input faces are then matched with the trained images in the database and once they match, the student will be marked as present and the ones who didn't match weremarked as absent in the class. Accuracy of 85% and 95% was obtained by testing the model with ten different faces with different facial expressions, angle and lighting conditions for LBPH algorithm and CNN (Convolutional neural networks) respectively.