Affiliation:
1. Department of Radio Electronics, Faculty of Electrical Engineering and Communication, Brno University of Technology, Technicka 3082/12, 61600 Brno, Czech Republic
2. EBIS, spol. s r.o., Krizikova 2962/70a, 61200 Brno, Czech Republic
Abstract
Face recognition has become an integral part of modern security processes. This paper introduces an optimization approach for the quantile interval method (QIM), a promising classifier learning technique used in face recognition to create face templates and improve recognition accuracy. Our research offers a three-fold contribution to the field. Firstly, (i) we strengthened the evidence that QIM outperforms other contemporary template creation approaches. For this reason, we investigate seven template creation methods, which include four cluster description-based methods and three estimation-based methods. Further, (ii) we extended testing; we use a nearly four times larger database compared to the previous study, which includes a new set, and we report the recognition performance on this extended database. Additionally, we distinguish between open- and closed-set identification. Thirdly, (iii) we perform an evaluation of the cluster estimation-based method (specifically QIM) with an in-depth analysis of its parameter setup in order to make its implementation feasible. We provide instructions and recommendations for the correct parameter setup. Our research confirms that QIM’s application in template creation improves recognition performance. In the case of automatic application and optimization of QIM parameters, improvement recognition is about 4–10% depending on the dataset. In the case of a too general dataset, QIM also provides an improvement, but the incorporation of QIM into an automated algorithm is not possible, since QIM, in this case, requires manual setting of optimal parameters. This research contributes to the advancement of secure and accurate face recognition systems, paving the way for its adoption in various security applications.
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Reference27 articles.
1. Hardware and software optimizations for accelerating deep neural networks: Survey of current trends, challenges, and the road ahead;Capra;IEEE Access,2020
2. A Survey on Efficient Convolutional Neural Networks and Hardware Acceleration;Ghimire;Electronics,2022
3. An Evaluation of Hardware-Efficient Quantum Neural Networks for Image Data Classification;Nguyen;Electronics,2022
4. (2022, November 01). AxisFD. Available online: https://www.axis.com/dam/public/d1/01/05/datasheet-axis-face-detector-en-US-358364.pdf.
5. (2022, October 03). HanwhaDEt. Available online: https://support.hanwhasecurity.com/hc/en-us/article_attachments/1260802483969/2._wisenet_ai_camera_white_paper_en_210317.pdf.