Sample selection for Face Identification System

Abstract

The face identification problem is the task of identifying incoming users from their face images. The applications of this task include automating user identification at the building entrance. Consequently, many proposed algorithms are proposed for the given task ranging from handcraft computational models to deep learning models. This paper utilizes the existing effective algorithms and proposes the template selection strategy to enhance its recognition performance when the enrollment of multiple samples for each user is proposed. Besides, the paper investigates the effect of image quality on the recognition performance of the system and the efficacy of the proposed template selection strategy when applied in such a situation. Experiments are performed on the in-house dataset collected from the Thai population--to evaluate the empirical system performance when the system is deployed in Thailand to automatically identify the user at the building gate--as well as on the LFW public dataset consisting of 13,000 face images of 5,749 individuals with multiple ethnicities. The results show that, with the proposed sample selection, the identification error on the in-house dataset for the close-set identification decreased from 3.11 to 2.46% at 0.11% and 0.06% FMR. For the open-set identification, the FNMR decreased from 9.36% to 5.40% at 6.20% and 3.23% FAR, respectively. In addition, the experiments on the LFW dataset have also demonstrated the efficacy of the proposed system consisting of the proposed sample selection method and the selection of face recognition modules. That is, the performance improvement is noticeable as compared to the baseline where the sample selection method is not deployed and to the previous work when multiple samples are used for enrollment. © 2022, School of Electrical Engineering and Informatics. All rights reserved.