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Analysis of Age Invariant Face Recognition Efficiency Using Face Feature Vectors

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Robotics, Computer Vision and Intelligent Systems (ROBOVIS 2024)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2077))

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Abstract

One of the main problems for face recognition when comparing photos of various ages is the impact of age progression on facial features. The face undergoes many changes as a person grows older, including geometrical changes and changes in facial hair, etc. Even though biometric markers such as computed face feature vectors should preferably be invariant to such factors, face recognition generally becomes less reliable as the age span grows larger. Therefore, this study was conducted with the aim of exploring the efficiency of such feature vectors in recognising individuals despite variations in age, and how to measure face recognition performance and behaviour in the data. It is shown that they are indeed discriminative enough to achieve age-invariant face recognition without synthesising age images through generative processes or training on specialised age related features.

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Acknowledgements

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 projects SNIC 2021/22-918 and SNIC 2022/22-1123.

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Authors and Affiliations

  1. Uppsala University, Uppsala, Sweden

    Anders Hast, Yijie Zhou, Congting Lai & Ivar Blohm

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  1. Anders Hast
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Correspondence to Anders Hast .

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  1. Polytechnic Institute of Setubal, Setúbal, Portugal

    Joaquim Filipe

  2. Biomimetics and Intelligent Systems, University of Oulu, OULU, Finland

    Juha Röning

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Hast, A., Zhou, Y., Lai, C., Blohm, I. (2024). Analysis of Age Invariant Face Recognition Efficiency Using Face Feature Vectors. In: Filipe, J., Röning, J. (eds) Robotics, Computer Vision and Intelligent Systems. ROBOVIS 2024. Communications in Computer and Information Science, vol 2077. Springer, Cham. https://doi.org/10.1007/978-3-031-59057-3_4

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