dr. C.G. Zeinstra (Chris)

Engineering & Materials Science

# Biometrics
# Classifiers
# Face Recognition
# Image Resolution
# Software Packages

Mathematics

# Classifier

Physics & Astronomy

# Confidence
# Intervals

Faculty of Science and Technology (TNW), Advanced Technology (AT)

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.

Bassit, A. , Hahn, F. , Zeinstra, C. , Veldhuis, R. , & Peter, A. (2021). Bloom Filter vs Homomorphic Encryption: Which approach protects the biometric data and satisfies ISO/IEC 24745? In BIOSIG 2021 - Proceedings of the 20th International Conference of the Biometrics Special Interest Group: 15.-17. September 2021 International Digital Conference IEEE. https://doi.org/10.1109/BIOSIG52210.2021.9548304

Zeinstra, C. , Meuwly, D. , Veldhuis, R. , & Spreeuwers, L. (2019). Mind the Gap: A practical framework for classifiers in a forensic context. In 2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems, BTAS 2018 [8698583] (IEEE nternational Conference on Biometrics Theory, Applications and Systems (BTAS); Vol. 2018). IEEE. https://doi.org/10.1109/BTAS.2018.8698583

Zeinstra, C. , & Haasnoot, E. (2018). Shallow CNNs for the Reliable Detection of Facial Marks. In A. Bromme, A. Uhl, C. Busch, C. Rathgeb, & A. Dantcheva (Eds.), 2018 International Conference of the Biometrics Special Interest Group, BIOSIG 2018 [8553157] (International Conference of the Biometrics Special Interest Group (BIOSIG); Vol. 2018). IEEE. https://doi.org/10.23919/BIOSIG.2018.8553157

Haasnoot, E., Khodabakhsh, A. , Zeinstra, C. , Spreeuwers, L. , & Veldhuis, R. (2018). FEERCI: A Package for Fast Non-Parametric Confidence Intervals for Equal Error Rates in Amortized O(m log n). In A. Bromme, A. Uhl, C. Busch, C. Rathgeb, & A. Dantcheva (Eds.), 2018 International Conference of the Biometrics Special Interest Group, BIOSIG 2018 [8553607] (International Conference of the Biometrics Special Interest Group (BIOSIG); Vol. 2018). IEEE. https://doi.org/10.23919/BIOSIG.2018.8553607

Zeinstra, C. , Veldhuis, R. , & Spreeuwers, L. (2018). Grid-Based Likelihood Ratio Classifiers for the Comparison of Facial Marks. IEEE transactions on information forensics and security, 13(1), 253-264. [8017453]. https://doi.org/10.1109/TIFS.2017.2746013

Zeinstra, C. G. , Meuwly, D., Ruifrok, A. C. C. , Veldhuis, R. N. J. , & Spreeuwers, L. J. (2018). Forensic face recognition as a means to determine strength of evidence: A survey. Forensic Science Review, 30(1), 21-32. http://forensicsciencereview.com/content.htm#v30

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