Abstract
Face recognition is a thriving topic in biometrics literature. In most cases, it is performed on RGB images using deep learning approaches. However, due to the wider scenarios in which face recognition can be applied, it is necessary to operate when illumination conditions are not in favor of the RGB image analysis. To overcome this problem, we present DLIF (Depth Landmark Identity by Fractal), a new depth face recognition method that uses fractal encoding to treat faces as depth features. A facial landmark predictor is developed to detect and extract the face, making DLIF completely in-depth. Comparisons of the fractal-encoded faces are obtained by the Canberra distance, which makes DLIF simple but effective. Statistical analysis is performed to improve DLIF, resulting in DLIF+, which takes into account the source of the image. DLIF and DLIF+ are tested on four challenging datasets and on a fifth dataset, named BIPS, with 72 subjects. The results obtained show that DLIF and DLIF+ are competitive with the state of the art and enhance the robustness of this method in relation to the number of subjects considered.
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Acknowledgements
This work was partially supported by the project IDA included in the Spoke 2 - Misinformation and Fakes of the Research and Innovation Program PE00000014, “SEcurity and RIghts in the CyberSpace (SERICS)”, under the National Recovery and Resilience Plan, Mission 4 “Education and Research” - Component 2 “From Research to Enterprise” - Investment 1.3, funded by the European Union - NextGenerationEU.
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Bilotti, U., Bisogni, C., Nappi, M., Pero, C. (2023). Depth Camera Face Recognition by Normalized Fractal Encodings. In: Foresti, G.L., Fusiello, A., Hancock, E. (eds) Image Analysis and Processing – ICIAP 2023. ICIAP 2023. Lecture Notes in Computer Science, vol 14233. Springer, Cham. https://doi.org/10.1007/978-3-031-43148-7_17
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