LITERATURE REVIEW: PENGENALAN WAJAH MENGGUNAKAN ALGORITMA CONVOLUTIONAL NEURAL NETWORK

Facial recognition to detect the identity of the gallon user's face in honesty in the school environment has many methods such as local, global, and hybrid approaches. The main problem of using the gallon of honesty is that the program uses the Self-service system, which is a self-service system, where the buyer serves itself unattended. The water charging activity is still found by users who are dishonest, such as taking water but not putting money into the place that has been provided, the thing that should be when the user fills the water then the user must also enter Money into the box provided. Because of the absence of supervision in this program of honesty then it is difficult to know who is dishonest in order to be able to do prevention for the dishonesty that has occurred when using the gallon of honesty program. Facial recognition using the Convolutional Neural Network (CNN) method to classify images. A literature review is used to analyse and focus on techniques in conducting facial recognition on the use of gallons of honesty. Keywords: facial recognition, convolutional neural network methods, a gallon of honest

Evaluation and Understandability of Face Image Quality Assessment

Face image quality assessment (FIQA) has been an area of interest to researchers as a way to improve the face recognition accuracy. By filtering out the low quality images we can reduce various difficulties faced in unconstrained face recognition, such as, failure in face or facial landmark detection or low presence of useful facial information. In last decade or so, researchers have proposed different methods to assess the face image quality, spanning from fusion of quality measures to using learning based methods. Different approaches have their own strength and weaknesses. But, it is hard to perform a comparative assessment of these methods without a database containing wide variety of face quality, a suitable training protocol that can efficiently utilize this large-scale dataset. In this thesis we focus on developing an evaluation platfrom using a large scale face database containing wide ranging face image quality and try to deconstruct the reason behind the predicted scores of learning based face image quality assessment methods. Contributions of this thesis is two-fold. Firstly, (i) a carefully crafted large scale database dedicated entirely to face image quality assessment has been proposed; (ii) a learning to rank based large-scale training protocol is devel- oped. Finally, (iii) a comprehensive study of 15 face image quality assessment methods using 12 different feature types, and relative ranking based label generation schemes, is performed. Evalua- tion results show various insights about the assessment methods which indicate the significance of the proposed database and the training protocol. Secondly, we have seen that in last few years, researchers have tried various learning based approaches to assess the face image quality. Most of these methods offer either a quality bin or a score summary as a measure of the biometric quality of the face image. But, to the best of our knowledge, so far there has not been any investigation on what are the explainable reasons behind the predicted scores. In this thesis, we propose a method to provide a clear and concise understanding of the predicted quality score of a learning based face image quality assessment. It is believed that this approach can be integrated into the FBI’s understandable template and can help in improving the image acquisition process by providing information on what quality factors need to be addressed