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.
Spreeuwers, L. (2021). Can face morphs be detected using face recognition systems? In Intergraf Currency+Identity Online 2021
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
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
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
Mehra, A. , Spreeuwers, L. , & Strisciuglio, N. (2021). Deepfake detection using capsule networks and long short-term memory networks. In G. M. Farinella, P. Radeva, J. Braz, & K. Bouatouch (Eds.), Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP (pp. 407-414). SCITEPRESS. https://doi.org/10.5220/0010289004070414
Gerats, B., Bouma, H., Uijens, W. , Englebienne, G. , & Spreeuwers, L. (2021). Individual action and group activity recognition in soccer videos from a static panoramic camera. In M. De Marsico, G. S. di Baja, & A. Fred (Eds.), ICPRAM 2021 - Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods (pp. 594-601). SCITEPRESS. https://doi.org/10.5220/0010303505940601
Raja, K., Ferrara, M., Franco, A. , Spreeuwers, L. , Batskos, I. , de Wit, F. F., Gomez-Barrero, M., Scherhag, U., Fischer, D., Venkatesh, S., Mohan Singh, J., Ramachandra, R., Rathgeb, C., Frings, D., Seidel, U., Knopjes, F. , Veldhuis, R. N. J., Maltoni, D., & Busch, C. (2021). Morphing Attack Detection - Database, Evaluation Platform and Benchmarking. IEEE transactions on information forensics and security, 16, 4336-4351. https://doi.org/10.1109/TIFS.2020.3035252, https://doi.org/10.1109/TIFS.2020.3035252