Abstract
This paper presents a novel automatic face recognition approach based on local binary patterns. This descriptor considers a local neighbourhood of a pixel to compute the feature vector values. This method is not very robust to handle image noise, variances and different illumination conditions. We address these issues by proposing a novel descriptor which considers more pixels and different neighbourhoods to compute the feature vector values. The proposed method is evaluated on two benchmark corpora, namely UFI and FERET face datasets. We experimentally show that our approach outperforms state-of-the-art methods and is efficient particularly in the real conditions where the above mentioned issues are obvious. We further show that the proposed method handles well one training sample issue and is also robust to the image resolution.
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Acknowledgment
This work has been supported by Cross-border Cooperation Program Czech Republic - Free State of Bavaria ETS Objective 2014-2020, project no. 211 - Modern Access to Historical Sources.
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Král, P., Vrba, A., Lenc, L. (2019). Enhanced Local Binary Patterns for Automatic Face Recognition. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2019. Lecture Notes in Computer Science(), vol 11509. Springer, Cham. https://doi.org/10.1007/978-3-030-20915-5_3
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DOI: https://doi.org/10.1007/978-3-030-20915-5_3
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