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
# Biometrics # Cameras # Classifiers # Experiments # Face Recognition # Image Resolution
Business & Economics
# Face Recognition # Likelihood Ratio
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), . 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
Spreeuwers, L. (2021). Can face morphs be detected using face recognition systems?. Paper presented at Intergraf Currency+Identity 2021, Online Conference.
Batskos, I. , de Wit, F. F. , Spreeuwers, L. , & Veldhuis, R. N. J. (2021). Preventing face morphing attacks by using legacy face images. IET biometrics, 10(4), 430-440. https://doi.org/10.1049/bme2.12047
Arican, T. , Veldhuis, R. N. J. , & Spreeuwers, L. (2021). Finger Vein Verification with a Convolutional Auto-encoder. In Proceedings of the 2021 Symposium on Information Theory and Signal Processing in the Benelux (pp. 43-51). Werkgemeenschap voor Informatie- en Communicatietheorie (WIC). http://www.w-i-c.org/proceedings/proceedings_SITB2021.pdf
van der Spek, M. , & Spreeuwers, L. (2021). Identification through Finger Bone Structure Biometrics. In R. van Sloun, & B. Skoric (Eds.), Proceedings of the 2021 Symposium on Information Theory and Signal Processing in the Benelux (pp. 56-63). Werkgemeenschap voor Informatie- en Communicatietheorie (WIC). http://www.w-i-c.org/proceedings/proceedings_SITB2021.pdf
Zeng, D. , Veldhuis, R. , Spreeuwers, L., & Arendsen, R. (2021). Occlusion-invariant face recognition using simultaneous segmentation. IET biometrics, 10(6), 679-691. https://doi.org/10.1049/bme2.12036
Zeng, D. , Veldhuis, R. , & Spreeuwers, L. (2021). A survey of face recognition techniques under occlusion. IET biometrics, 10(6), 581-606. https://doi.org/10.1049/bme2.12029
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Courses Academic Year 2021/2022
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.