Publication detail

Optimized Classifier Learning for Face Recognition Performance Boost in Security and Surveillance Applications

POMĚNKOVÁ, J. MALACH, T.

Original Title

Optimized Classifier Learning for Face Recognition Performance Boost in Security and Surveillance Applications

Type

journal article in Web of Science

Language

English

Original 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.

Keywords

template creation; surveillance face recognition; classifier learning; parameter optimization; security application

Authors

POMĚNKOVÁ, J.; MALACH, T.

Released

7. 8. 2023

Publisher

MDPI

Location

Basel, Switzerland

ISBN

1424-8220

Periodical

SENSORS

Year of study

23

Number

15

State

Swiss Confederation

Pages from

1

Pages to

21

Pages count

21

URL

https://www.mdpi.com/1424-8220/23/15/7012

Full text in the Digital Library

http://hdl.handle.net/11012/245019

BibTex

@article{BUT184339,
  author="Jitka {Dluhá} and Tobiáš {Malach}",
  title="Optimized Classifier Learning for Face Recognition Performance Boost in Security and Surveillance Applications",
  journal="SENSORS",
  year="2023",
  volume="23",
  number="15",
  pages="1--21",
  doi="10.3390/s23157012",
  issn="1424-8220",
  url="https://www.mdpi.com/1424-8220/23/15/7012"
}