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.

Department of Radio Electronics Faculty of Electrical Engineering and Communication Brno University of Technology Technicka 3082 12 61600 Brno Czech Republic

EBIS spol s r o Krizikova 2962 70a 61200 Brno Czech Republic

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Capra M., Bussolino B., Marchisio A., Masera G., Martina M., Shafique M. Hardware and software optimizations for accelerating deep neural networks: Survey of current trends, challenges, and the road ahead. IEEE Access. 2020;8:225134–225180.

Ghimire D., Kil D., Kim S.-H. A Survey on Efficient Convolutional Neural Networks and Hardware Acceleration. Electronics. 2022;11:945.

Nguyen T., Paik I., Watanobe Y., Thang T.C. An Evaluation of Hardware-Efficient Quantum Neural Networks for Image Data Classification. Electronics. 2022;11:437.

AxisFD. [(accessed on 1 November 2022)]. Available online: https://www.axis.com/dam/public/d1/01/05/datasheet-axis-face-detector-en-US-358364.pdf.

HanwhaDEt. [(accessed on 3 October 2022)]. Available online: https://support.hanwhasecurity.com/hc/en-us/article_attachments/1260802483969/2._wisenet_ai_camera_white_paper_en_210317.pdf.

Axis. 2022. [(accessed on 1 November 2022)]. Available online: https://www.axis.com/developer-community/acap.

Varga D., Havasi L., Szirányi T. Pedestrian detection in surveillance videos based on CS-LBP feature; Proceedings of the International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS); Budapest, Hungary. 3–5 June 2015; pp. 413–417.

Yang Z., Ai H. Demographic Classification with Local Binary Patterns. In: Lee S.W., Li S.Z., editors. Advances in Biometrics, ICB 2007. Volume 4642. Springer; Berlin/Heidelberg, Germany: 2007. pp. 464–473.

Heikkilä M., Pietikäinen M., Schmid C. Description of Interest Regions with Center-Symmetric Local Binary Patterns. In: Kalra P.K., Peleg S., editors. Computer Vision, Graphics and Image Processing. Volume 4338. Springer; Berlin/Heidelberg, Germany: 2006. pp. 58–69.

Nanni L., Lumini A. Local binary patterns for a hybrid fingerprint matcher. Pattern Recognit. 2008;41:3461–3466. doi: 10.1016/j.patcog.2008.05.013. DOI

Malach T., Pomekova J. Optimal face templates: The next step in surveillance face recognition. Pattern Anal. Appl. 2019;7:1021–1032.

Stallkamp J., Ekenel H.K., Stiefelhagen R. Video-based Face Recognition on Real-World Data; Proceedings of the IEEE 11th International Conference on Computer Vision, ICCV 2007; Rio de Janeiro, Brazil. 14–21 October 2007; pp. 1–8.

Malach T., Prinosil J. Face templates creation surveillance face recognition system; Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods, ICPRAM; Angers, France. 6–8 March 2014; pp. 724–729.

Bambuch P., Malach T., Malach J. Video database for face recognition; Proceedings of the Technical Computing, TCB 2012; Bratislava, Slovakia. 7 November 2014; [(accessed on 15 November 2022)]. 7p. Available online: https://dsp.vscht.cz/konference_matlab/MATLAB12/full_paper/012_Bambuch.pdf.

Phillips P.J., Alvin M., Wilson C.L., Przybocki M. An introduction evaluating biometric systems. Computer. 2002;33:56–63. doi: 10.1109/2.820040. DOI

Viola P., Jones M. Robust Real-time Object Detection. Int. J. Comput. Vis. 2001;4:34–47.

Singh C., Mittal N., Walia E. Complementary feature sets for optimal face recognition. EURASIP J. Image Video Process. 2014;35:1687–5281. doi: 10.1186/1687-5281-2014-35. DOI

Huang G.B., Lee H., Learned-Miller E. Learning hierarchical representations for face verification with convolutional deep belief networks; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; Providence, RI, USA. 16–21 June 2012; pp. 2518–2525.

Liu H., Xia K., Li T., Ma J., Owoola E. Dimensionality reduction of hyperspectral images based on improved spatial–spectral weight manifold embedding. Sensors. 2020;16:4413. doi: 10.3390/s20164413. PubMed DOI PMC

Malach T., Pomenkova J. Comparing Classifier’s Performance Based on Confidence Interval of the ROC. Radioengineering. 2018;27:827–834. doi: 10.13164/re.2018.0827. DOI

Struyf A., Hubert M., Rousseuw P. Clustering in an Object-Oriented Environment. J. Stat. Softw. 1997;1:1–30. doi: 10.18637/jss.v001.i04. DOI

Prinosil J. Local descriptors based face recognition engine for video surveillance systems; Proceedings of the 36th International Conference on Telecommunications and Signal Processing, TSP 2013; Berlin, Germany. 2–4 July 2013; pp. 862–866.

Theodoridis S., Koutroubas K. Pattern Recognition. Academic Press; Waltham, MA, USA: 2009.

Phillips P.J., Hyeonjoon M., Rizvi S.A., Rauss P.J. The FERET evaluation methodology for face-recognition algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 2000;22:1090–1104. doi: 10.1109/34.879790. DOI

Huang B.G., Ramesh M., Berg T., Learned-Miller E. Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments. University of Massachusetts; Amherst, MA, USA: 2007. Technical Report 07–49.

Phillips P.J., Flynn P.J., Beveridge J.R., Scruggs W.T., O’Toole A.J., Bolme D., Bowyer K.W., Draper B.A., Givens G.H., Lui Y., et al. Overview of the Multiple Biometrics Grand Challenge. Adv. Biom. 2009;5558:705–714.

The MathWorks, Inc. Box Plots. 2020. [(accessed on 16 May 2020)]. Available online: https://www.mathworks.com/help/stats/box-plots.html.