| A guideline for the validation of likelihood ratio methods used for forensic evidence evaluation D Meuwly, D Ramos, R Haraksim Forensic science international 276, 142-153, 2017 | 193 | 2017 |
| Faceqnet: Quality assessment for face recognition based on deep learning J Hernandez-Ortega, J Galbally, J Fierrez, R Haraksim, L Beslay 2019 International Conference on Biometrics (ICB), 1-8, 2019 | 181 | 2019 |
| A study of age and ageing in fingerprint biometrics J Galbally, R Haraksim, L Beslay IEEE Transactions on Information Forensics and Security 14 (5), 1351-1365, 2018 | 93 | 2018 |
| Likelihood ratio data to report the validation of a forensic fingerprint evaluation method D Ramos, R Haraksim, D Meuwly Data in brief 10, 75-92, 2017 | 41 | 2017 |
| Measuring coherence of computer-assisted likelihood ratio methods R Haraksim, D Ramos, D Meuwly, CEH Berger Forensic Science International 249, 123-132, 2015 | 38 | 2015 |
| Validation of forensic automatic likelihood ratio methods D Ramos, D Meuwly, R Haraksim, CEH Berger Handbook of forensic statistics, 143-162, 2020 | 34 | 2020 |
| Fingerprint growth model for mitigating the ageing effect on children’s fingerprints matching R Haraksim, J Galbally, L Beslay Pattern Recognition 88, 614-628, 2019 | 22 | 2019 |
| Investigation of portability of space docking techniques for autonomous underwater docking F Maurelli, Y Petillot, A Mallios, S Krupinski, R Haraksim, P Sotiropoulos OCEANS 2009-EUROPE, 1-9, 2009 | 22 | 2009 |
| Study on face identification technology for its implementation in the Schengen information system J Galbally, P Ferrara, R Haraksim, A Psyllos, L Beslay Publications Office of the European Union, 2019 | 19 | 2019 |
| Biometric evidence in forensic automatic speaker recognition A Drygajlo, R Haraksim Handbook of Biometrics for Forensic Science, 221-239, 2017 | 16 | 2017 |
| Validation of likelihood ratio methods used for forensic evidence evaluation: application in forensic fingerprints R Haraksim | 15 | 2014 |
| Fingermark quality assessment framework with classic and deep learning ensemble models T Oblak, R Haraksim, P Peer, L Beslay Knowledge-Based Systems 250, 109148, 2022 | 13 | 2022 |
| Validation of likelihood ratio methods for forensic evidence evaluation handling multimodal score distributions R Haraksim, D Ramos, D Meuwly IET biometrics 6 (2), 61-69, 2017 | 12 | 2017 |
| Altered fingerprint detection–algorithm performance evaluation R Haraksim, A Anthonioz, C Champod, M Olsen, J Ellingsgaard, ... 2016 4th International Conference on Biometrics and Forensics (IWBF), 1-6, 2016 | 10 | 2016 |
| Study on fingermark and palmmark identification technologies for their implementation in the schengen information system R Haraksim, J Galbally, L Beslay Luxembourg: Publication office of the European Union. Doi 10, 852462, 2019 | 9 | 2019 |
| Fingerprint quality: A lifetime story J Galbally, R Haraksim, L Beslay 2018 International Conference of the Biometrics Special Interest Group …, 2018 | 9 | 2018 |
| Multiple AUV control in an operational context: a leader-follower approach R Haraksim, L Brignone, J Opderbecke OCEANS 2009-EUROPE, 1-6, 2009 | 8 | 2009 |
| Fingermark quality assessment: An open-source toolbox T Oblak, R Haraksim, L Beslay, P Peer 2021 International Conference of the Biometrics Special Interest Group …, 2021 | 7 | 2021 |
| Fingerprint quality: Mapping NFIQ1 classes and NFIQ2 values J Galbally, R Haraksim, P Ferrara, L Beslay, E Tabassi 2019 International Conference on Biometrics (ICB), 1-8, 2019 | 7 | 2019 |
| Assignment of the evidential value of a fingermark general pattern using a Bayesian network R Haraksim, D Meuwly, G Doekhie, P Vergeer, M Sjerps 2013 International Conference of the BIOSIG Special Interest Group (BIOSIG …, 2013 | 7 | 2013 |
