Face Recognition in the Wild for Secure Authentication with Open Set Approach

Abstract

In everyday life, authentication is an indispensable process of human activities. Bio-metric authentication system is one of the effective solutions, because it uses human-based features, instead of other traditional features, such as pin, password, etc. However, to apply a face authentication system in practical applications, we need to ensure that the system must not try to recognize the face of an unknown person into known categories, meaning we need to reject faces of unknown people in our application. In this paper, we present the limitations of recent Deep Learning based methods in Face Recognition tasks. We then propose two methods helping Face Recognition system have the ability to reject faces from unknown people by using Open-Set concepts. We conduct the experiments on a subset of CASIA-WebFace dataset, with a train set that includes 7000 images of 100 known people and a test set that includes both known and unknown people. Without rejecting unknown faces, the regular face recognition, i.e. the baseline method, yields the accuracy of only 45.9%, as the method tries to classify all face photos into known classes. Our proposed methods, which are combined deep network of Facenet system with recent Open Set methods, are called Learning Placeholder on Facenet (P-Facenet) and Facenet with OpenMax (O-Facenet). They achieve the accuracy of 83.6% and 88.5% respectively. This is a potential approach for authentication with face recognition to decrease the error rate of the model when recognizing faces of unknown people in the wild.