dr.ir. L.J. Spreeuwers (Luuk)

Associate Professor

About Me

Dr. Luuk Spreeuwers studied Electrical Engineering at the University of Twente, Netherlands. In 1992 he obtained his PhD from the University of Twente. The title of his PhD-thesis is: Image Filtering with Neural Networks: Applications and Performance Evaluation. Subsequently Luuk Spreeuwers worked at the International Institute for Aerospace and Earth Sciences (ITC) in Enschede, Netherlands, the University of Twente in a SION project on 3-D image analysis of aerial image sequences and in Budapest at the Hungarian Academy of Sciences in a 3-D textures ERCIM project. From 1999-2005 Luuk Spreeuwers worked on 3-D modelling and segmentation of the human heart in MRI at the Image Sciences Institute of the University Medical Centre in Utrecht, the Netherlands. Currently, he is an Associate Professor at the Data Management and Biometrics Group of the Department of EEMCS of the University of Twente, Netherlands where he is the leader of the biometrics sub-group. In addition, he is the programme mentor of the Computer Vision and Biometrics Master Specialisation and is actively involved in teaching Bachelor's and Master's courses and president of the EE Programme Committee. He was nominated 6 times for the EE Educational award and won the prize in 2018. Luuk Spreeuwers has published over 100 papers in international conferences and journals. In 2015 he published a paper on 3D face recognition where he claimed the world's best performance on a recognised benchmark in 3D face recognition. In 2017 he won the best paper award at the BIOSIG conference with the paper titled: "De-Duplication using automated Face Recognition. An accurate mathematical Model and all Babies are equally cute". He was and is involved in numerous national and European projects among which 3DFace, SOTAMD and currently iMARS. His expertise involves digital image processing and analysis, medical image analysis, biometrics and pattern recognition in general.


Engineering & Materials Science
Face Recognition
Image Resolution
Business & Economics
Face Recognition
Likelihood Ratio


Kelly, U. M. , Spreeuwers, L. , & Veldhuis, R. (2023). Exploring Face De-Identification using Latent Spaces. In 2022 IEEE International Joint Conference on Biometrics (IJCB) (pp. 1-7). [10007990] (IEEE International Joint Conference on Biometrics (IJCB); Vol. 2022). IEEE. https://doi.org/10.1109/IJCB54206.2022.10007990
Ferla, R. , Spreeuwers, L. , & Zeinstra, C. G. (2022). Exploring the GANformer for Face Generation: Investigating the segmentation and smile augmentation potential. In Proceedings of the 2022 Symposium on Information Theory and Signal Processing in the Benelux (pp. 38)
Bunda, S. , Spreeuwers, L. , & Zeinstra, C. (2022). Sub-byte quantization of Mobile Face Recognition Convolutional Neural Networks. In 2022 International Conference of the Biometrics Special Interest Group (BIOSIG) (pp. 1-5). [9897025] (International Conference of the Biometrics Special Interest Group (BIOSIG); Vol. 2022). IEEE. https://doi.org/10.1109/BIOSIG55365.2022.9897025
de Wit, F. F. , Spreeuwers, L. , & Zeinstra, C. G. (2022). Biometric Testing: aligning standards and practice. 26-32. Paper presented at 42nd WIC Symposium on Information Theory and Signal Processing in the Benelux, SITB 2022, Louvain-la Neuve, Belgium.
Arican, T. , Veldhuis, R. , & Spreeuwers, L. (2022). Fingers Crossed: An Analysis of Cross-Device Finger Vein Recognition. In A. Bromme, N. Damer, M. Gomez-Barrero, K. Raja, C. Rathgeb, A. F. Sequeira, M. Todisco, & A. Uhl (Eds.), 2022 International Conference of the Biometrics Special Interest Group (BIOSIG) IEEE. https://doi.org/10.1109/BIOSIG55365.2022.9897029
Ramesh, D. S., Heijnen, S., Hekster, O. , Spreeuwers, L. , & de Wit, F. (2022). Facial recognition as a tool to identify Roman emperors: towards a new methodology. Humanities and Social Sciences Communications, 9(1), [78]. https://doi.org/10.1057/s41599-022-01090-y
Spreeuwers, L., Schils, M. , Veldhuis, R. , & Kelly, U. (2022). Practical Evaluation of Face Morphing Attack Detection Methods. In C. Rathgeb, R. Tolosana, R. Vera-Rodriguez, & C. Busch (Eds.), Handbook of Digital Face Manipulation and Detection: From DeepFakes to Morphing Attacks (pp. 351-365). (Advances in Computer Vision and Pattern Recognition). Springer Science + Business Media. https://doi.org/10.1007/978-3-030-87664-7_16

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Courses Academic Year  2022/2023

Courses in the current academic year are added at the moment they are finalised in the Osiris system. Therefore it is possible that the list is not yet complete for the whole academic year.

Courses Academic Year  2021/2022

Contact Details

Visiting Address

University of Twente
Faculty of Electrical Engineering, Mathematics and Computer Science
Zilverling (building no. 11), room 4067
Hallenweg 19
7522NH  Enschede
The Netherlands

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Mailing Address

University of Twente
Faculty of Electrical Engineering, Mathematics and Computer Science
Zilverling  4067
P.O. Box 217
7500 AE Enschede
The Netherlands