Convolution neural network for face similarity estimation

• Autorzy: Jankowiak Adam , Domski Wojciech

Identyfikatory

DOI

10.14313/jamris‐2025‐007

• Języki publikacji: EN

Abstrakty

EN

We present a convolution neural network used to determine face similarity given two images as input, i.e. a face identification task. The main focus is on the shape of the input data. We propose schemes where two pictures are connected in four different ways. The input sample is concatenated horizontally and vertically, giving the first two schemes. The other two input shapes include the intertwining by column and by row. Analysis of precision versus recall has been provided for each input schema. Some of the traditional approaches focus on deriving the feature vectors of an individual and then comparing the obtained vectors with each other. Our paper offers a new approach to face identification problems where two images of an individual are directly fed to the neural network. Then, it is the task of the neural network to determine the similarity score.

Słowa kluczowe

EN

CNN; face identification; input shaping

• Wydawca: Łukasiewicz Industrial Research Institute for Automation and Measurements PIAP
• Czasopismo: Journal of Automation Mobile Robotics and Intelligent Systems
• Rocznik: 2025
• Tom: Vol. 19, No. 1
• Strony: 65--71
• Opis fizyczny: Bibliogr. 16 poz., rys.

Twórcy

autor

Jankowiak Adam

• https://orcid.org/0009-0009-1587-7394

autor

Domski Wojciech

• Department of Cybernetics and Robotics, Wrocław University of Science and Technology, ul.Janiszewskiego 11/17, 50-372 Wrocław, Poland, www.edu.domski.pl

• https://orcid.org/0000-0001-5768-8051

Bibliografia

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• [13] Y. Taigman, M. Yang, M. Ranzato, and L. Wolf, “Deepface: Closing the gap to human-level performance in face verification”. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014, 1701–1708, 10.1109/CVPR.2014.220.
• [14] M. Turk and A. Pentland, “Face recognition using eigenfaces”. In: Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1991, 586–591, 10.1109/CVPR.1991.139758.
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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).