Artificially Intelligent Bot

Abstract

Face is the representation of one's identity. Hence, we have proposed an automated attendance system based on face recognition. Face recognition system is very useful in life applications especially in security control systems. The airpoli protection system uses face recognition to identify suspects and FBI (Federal Bureau of Investigation) uses face recognition for criminal investigations. In our proposed approach, firstly, video framing is performed by activating the camera through a user- friendly interface. The face ROI is detected and segmented from the ideo frame by using Viola-Jones algorithm. In the preprocessing stage, scaling ofthe size of images is performed if necessary, in order to prevent loss of information. The median filtering is applied to remove noise followed by convers ion of colour images to grayscale images. After that, contrast- limited adaptive histogram equalization (CLAHE) is implemented on images to enhance the contrast of images. In face recognition stage, enhanced local binary pattern (LBP) and principal component analysis (PCA) is applied correspondingly in order to extract the features from facial images. In our proposed approach, the enhanced local binary pattern outperform the original LBP by reducing the illumination effect and increasing the recognition rate. Next, the features extracted from the test images are compared with the features extracted from the training images. The facial images are then classified and recognized based on the best result obtained from the combinat inn of algorithm, enhanced LBP and PCA. Finally, the attendance of the recognized student will be marked and saved in the excel file. The student who is not registered \ ill also be able to register on the spot and notification wi ll be given if students sign in more Lhan once. The average accuracy of recognition is 100 % for good quality images, 94.12 % of low-quality images and 95.76 % for Yale face database when two images per person are trained.