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Age-Invariant Face Recognition Using Face Feature Vectors and Embedded Prototype Subspace Classifiers

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Advanced Concepts for Intelligent Vision Systems (ACIVS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14124))

Abstract

One of the major difficulties in face recognition while comparing photographs of individuals of different ages is the influence of age progression on their facial features. As a person ages, the face undergoes many changes, such as geometrical changes, changes in facial hair, and the presence of glasses, among others. Although biometric markers like computed face feature vectors should ideally remain unchanged by such factors, face recognition becomes less reliable as the age range increases. Therefore, this investigation was carried out to examine how the use of Embedded Prototype Subspace Classifiers could improve face recognition accuracy when dealing with age-related variations using face feature vectors only.

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Acknowledgments

This work has been partially supported by the Swedish Research Council (Dnr 2020-04652; Dnr 2022-02056) in the projects The City’s Faces. Visual culture and social structure in Stockholm 1880-1930 and The International Centre for Evidence-Based Criminal Law (EB-CRIME). The computations were performed on resources provided by SNIC through UPPMAX under project SNIC 2021/22-918.

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Authors and Affiliations

  1. Uppsala University, 751 05, Uppsala, Sweden

    Anders Hast

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  1. Anders Hast
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  1. DGA TA, Toulouse, France

    Jaques Blanc-Talon

  2. University of Auckland, Auckland, New Zealand

    Patrice Delmas

  3. Ghent University, Ghent, Belgium

    Wilfried Philips

  4. University of Antwerp, Wilrijk, Belgium

    Paul Scheunders

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Hast, A. (2023). Age-Invariant Face Recognition Using Face Feature Vectors and Embedded Prototype Subspace Classifiers. In: Blanc-Talon, J., Delmas, P., Philips, W., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2023. Lecture Notes in Computer Science, vol 14124. Springer, Cham. https://doi.org/10.1007/978-3-031-45382-3_8

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  • DOI: https://doi.org/10.1007/978-3-031-45382-3_8

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