{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T13:59:21Z","timestamp":1774619961115,"version":"3.50.1"},"reference-count":74,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2021,11,17]],"date-time":"2021-11-17T00:00:00Z","timestamp":1637107200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000780","name":"European Commission","doi-asserted-by":"publisher","award":["H2020-MSCA-ITN-2019-860315"],"award-info":[{"award-number":["H2020-MSCA-ITN-2019-860315"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000780","name":"European Commission","doi-asserted-by":"publisher","award":["H2020-MSCA-ITN-2019-860813"],"award-info":[{"award-number":["H2020-MSCA-ITN-2019-860813"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000780","name":"European Commission","doi-asserted-by":"publisher","award":["H2020;IMI2-2018-15-853981"],"award-info":[{"award-number":["H2020;IMI2-2018-15-853981"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003329","name":"Ministry of Economy, Industry and Competitiveness","doi-asserted-by":"publisher","award":["RTI2018-101248-B-I00 MINECO\/FEDER"],"award-info":[{"award-number":["RTI2018-101248-B-I00 MINECO\/FEDER"]}],"id":[{"id":"10.13039\/501100003329","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003329","name":"Ministry of Economy, Industry and Competitiveness","doi-asserted-by":"publisher","award":["RTI2018-095232-B-C22"],"award-info":[{"award-number":["RTI2018-095232-B-C22"]}],"id":[{"id":"10.13039\/501100003329","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>Machine learning methods are growing in relevance for biometrics and personal information processing in domains such as forensics, e-health, recruitment, and e-learning. In these domains, white-box (human-readable) explanations of systems built on machine learning methods become crucial. Inductive logic programming (ILP) is a subfield of symbolic AI aimed to automatically learn declarative theories about the processing of data. Learning from interpretation transition (LFIT) is an ILP technique that can learn a propositional logic theory equivalent to a given black-box system (under certain conditions). The present work takes a first step to a general methodology to incorporate accurate declarative explanations to classic machine learning by checking the viability of LFIT in a specific AI application scenario: fair recruitment based on an automatic tool generated with machine learning methods for ranking Curricula Vitae that incorporates soft biometric information (gender and ethnicity). We show the expressiveness of LFIT for this specific problem and propose a scheme that can be applicable to other domains. In order to check the ability to cope with other domains no matter the machine learning paradigm used, we have done a preliminary test of the expressiveness of LFIT, feeding it with a real dataset about adult incomes taken from the US census, in which we consider the income level as a function of the rest of attributes to verify if LFIT can provide logical theory to support and explain to what extent higher incomes are biased by gender and ethnicity.<\/jats:p>","DOI":"10.3390\/computers10110154","type":"journal-article","created":{"date-parts":[[2021,11,17]],"date-time":"2021-11-17T09:16:11Z","timestamp":1637140571000},"page":"154","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Symbolic AI for XAI: Evaluating LFIT Inductive Programming for Explaining Biases in Machine Learning"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7100-1690","authenticated-orcid":false,"given":"Alfonso","family":"Ortega","sequence":"first","affiliation":[{"name":"Departamento de Inform\u00e1tica, Universidad de Oviedo, 33204 Oviedo, Spain"},{"name":"Escuela Polit\u00e9cnica Superior, Universidad Aut\u00f3noma de Madrid, 28049 Madrid, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6343-5656","authenticated-orcid":false,"given":"Julian","family":"Fierrez","sequence":"additional","affiliation":[{"name":"Escuela Polit\u00e9cnica Superior, Universidad Aut\u00f3noma de Madrid, 28049 Madrid, Spain"}]},{"given":"Aythami","family":"Morales","sequence":"additional","affiliation":[{"name":"Escuela Polit\u00e9cnica Superior, Universidad Aut\u00f3noma de Madrid, 28049 Madrid, Spain"}]},{"given":"Zilong","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute IMDEA Software, 28223 Pozuelo de Alarc\u00f3n, Spain"}]},{"given":"Marina","family":"de la Cruz","sequence":"additional","affiliation":[{"name":"Escuela Superior de Ingenier\u00eda y Tecnolog\u00eda (ESIT), Universidad Internacional de la Rioja, 26006 Logro\u00f1o, Spain"}]},{"given":"C\u00e9sar Luis","family":"Alonso","sequence":"additional","affiliation":[{"name":"Departamento de Inform\u00e1tica, Universidad de Oviedo, 33204 Oviedo, Spain"}]},{"given":"Tony","family":"Ribeiro","sequence":"additional","affiliation":[{"name":"Laboratoire des Sciences du Num\u00e9rique de Nantes, 44300 Nantes, France"},{"name":"National Institute of Informatics, Tokyo 101-8430, Japan"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1109\/MSP.2012.2205597","article-title":"Deep neural networks for acoustic modeling in speech recognition","volume":"29","author":"Senior","year":"2012","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Fei-Fei, L. (2009, January 20\u201325). ImageNet: A Large-Scale Hierarchical Image Database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_3","unstructured":"Wu, Y., Schuster, M., Chen, Z., Le, Q.V., Norouzi, M., Macherey, W., and Klingner, J. (2016). Google\u2019s neural machine translation system: Bridging the gap between human and machine translation. arXiv."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1038\/s41586-019-1138-y","article-title":"Machine behaviour","volume":"568","author":"Rahwan","year":"2019","journal-title":"Nature"},{"key":"ref_5","unstructured":"Serna, I., Morales, A., Fierrez, J., Cebrian, M., Obradovich, N., and Rahwan, I. (2020, January 7). Algorithmic Discrimination: Formulation and Exploration in Deep Learning-based Face Biometrics. Proceedings of the AAAI Workshop on Artificial Intelligence Safety (SafeAI), New York, NY, USA."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.forsciint.2015.09.002","article-title":"Facial Soft Biometric Features for Forensic Face Recognition","volume":"257","author":"Tome","year":"2015","journal-title":"Forensic Sci. Int."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Loyola-Gonzalez, O., Ferreira, E.F., Morales, A., Fierrez, J., Medina-Perez, M.A., and Monroy, R. (2021). Impact of Minutiae Errors in Latent Fingerprint Identification: Assessment and Prediction. Appl. Sci., 11.","DOI":"10.3390\/app11094187"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Pe\u00f1a, A., Serna, I., Morales, A., and Fierrez, J. (2020, January 14\u201319). Bias in Multimodal AI: Testbed for Fair Automatic Recruitment. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, Seattle, WA, USA.","DOI":"10.1109\/CVPRW50498.2020.00022"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Terhorst, P., Kolf, J.N., Huber, M., Kirchbuchner, F., Damer, N., Morales, A., Fierrez, J., and Kuijper, A. (2021). A Comprehensive Study on Face Recognition Biases Beyond Demographics. arXiv.","DOI":"10.1109\/TTS.2021.3111823"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Serna, I., Pe\u00f1a, A., Morales, A., and Fierrez, J. (2021, January 10\u201315). InsideBias: Measuring Bias in Deep Networks and Application to Face Gender Biometrics. Proceedings of the IAPR International Conference on Pattern Recognition (ICPR), Milan, Italy.","DOI":"10.1109\/ICPR48806.2021.9412443"},{"key":"ref_11","unstructured":"Serna, I., Morales, A., Fierrez, J., and Ortega-Garcia, J. (2021). IFBiD: Inference-Free Bias Detection. arXiv."},{"key":"ref_12","unstructured":"Sleeman, D.H. (1988, January 3\u20135). Machine Learning in the Next Five Years. Proceedings of the Third European Working Session on Learning, EWSL 1988, Glasgow, UK."},{"key":"ref_13","first-page":"52","article-title":"How Does Predicate Invention Affect Human Comprehensibility?","volume":"Volume 10326","author":"Cussens","year":"2016","journal-title":"Proceedings of the Inductive Logic Programming\u201426th International Conference (ILP 2016)"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1119","DOI":"10.1007\/s10994-018-5707-3","article-title":"Ultra-Strong Machine Learning: Comprehensibility of programs learned with ILP","volume":"107","author":"Muggleton","year":"2018","journal-title":"Mach. Learn."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.inffus.2019.12.012","article-title":"Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI","volume":"58","author":"Arrieta","year":"2020","journal-title":"Inf. Fusion"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1007\/BF03037089","article-title":"Inductive Logic Programming","volume":"8","author":"Muggleton","year":"1991","journal-title":"New Gener. Comput."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1007\/s10994-013-5358-3","article-title":"Meta-interpretive learning: Application to grammatical inference","volume":"94","author":"Muggleton","year":"1994","journal-title":"Mach. Learn."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1063","DOI":"10.1007\/s10994-018-5712-6","article-title":"Learning efficient logic programs","volume":"108","author":"Cropper","year":"2019","journal-title":"Mach. Learn."},{"key":"ref_19","unstructured":"Dai, W.Z., Muggleton, S.H., and Zhou, Z.H. (2015, January 20\u201322). Logical Vision: Meta-Interpretive Learning for Simple Geometrical Concepts. Proceedings of the 25th International Conference on Inductive Logic Programming, Kyoto, Japan."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1097","DOI":"10.1007\/s10994-018-5710-8","article-title":"Meta-Interpretive Learning from noisy images","volume":"107","author":"Muggleton","year":"2018","journal-title":"Mach. Learn."},{"key":"ref_21","unstructured":"Ribeiro, T. (2015). Studies on Learning Dynamics of Systems from State Transitions. [Ph.D. Thesis, The Graduate University for Advanced Studies]."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Ortega, A., Fierrez, J., Morales, A., Wang, Z., and Ribeiro, T. (2021, January 5\u20139). Symbolic AI for XAI: Evaluating LFIT Inductive Programming for Fair and Explainable Automatic Recruitment. Proceedings of the IEEE Winter Conference on Applications of Computer Vision Workshops, WACV Workshops 2021, Waikola, HI, USA.","DOI":"10.1109\/WACVW52041.2021.00013"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Eiben, A., and Smith, J. (2003). Introduction To Evolutionary Computing, Springer.","DOI":"10.1007\/978-3-662-05094-1"},{"key":"ref_24","unstructured":"O\u2019Neill, M., and Conor, R. (2003). Grammatical Evolution\u2014Evolutionary Automatic Programming in an Arbitrary Language, Kluwer. Genetic Programming."},{"key":"ref_25","first-page":"182","article-title":"Attribute Grammar Evolution","volume":"Volume 3562","author":"Mira","year":"2005","journal-title":"Artificial Intelligence and Knowledge Engineering Applications: A Bioinspired Approach: First International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2005, Las Palmas, Canary Islands, Spain, 15\u201318 June 2005, Proceedings, Part II"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1109\/TEVC.2006.880327","article-title":"Christiansen Grammar Evolution: Grammatical Evolution With Semantics","volume":"11","author":"Ortega","year":"2007","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"757","DOI":"10.1142\/S0218213009000391","article-title":"A New Linear Genetic Programming Approach Based on Straight Line Programs: Some Theoretical and Experimental Aspects","volume":"18","author":"Alonso","year":"2009","journal-title":"Int. J. Artif. Intell. Tools"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1613\/jair.5714","article-title":"Learning Explanatory Rules from Noisy Data","volume":"61","author":"Evans","year":"2017","journal-title":"J. Artif. Intell. Res."},{"key":"ref_29","unstructured":"Manhaeve, R., Dumancic, S., Kimmig, A., Demeester, T., and De Raedt, L. (2019). DeepProbLog: Neural Probabilistic Logic Programming. arXiv."},{"key":"ref_30","unstructured":"Doran, D., Schulz, S., and Besold, T. (2017). What Does Explainable AI Really Mean? A New Conceptualization of Perspectives. arXiv."},{"key":"ref_31","unstructured":"Hailesilassie, T. (2016). Rule Extraction Algorithm for Deep Neural Networks: A Review. arXiv."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zilke, J.R. (2016). Extracting Rules from Deep Neural Networks. arXiv.","DOI":"10.1007\/978-3-319-46307-0_29"},{"key":"ref_33","first-page":"33","article-title":"Integration of numeric and symbolic information for semantic image interpretation","volume":"10","author":"Donadello","year":"2016","journal-title":"Intell. Artif."},{"key":"ref_34","unstructured":"Donadello, I., and Dragoni, M. (2020, January 25\u201326). SeXAI: Introducing Concepts into Black Boxes for Explainable Artificial Intelligence. Proceedings of the XAI.it@AI*IA 2020 Italian Workshop on Explainable Artificial Intelligence, Online."},{"key":"ref_35","unstructured":"Yuan, H., Yu, H., Gui, S., and Ji, S. (2020). Explainability in Graph Neural Networks: A Taxonomic Survey. arXiv."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Gilpin, L.H., Bau, D., Yuan, B.Z., Bajwa, A., Specter, M.A., and Kagal, L. (2018, January 1\u20133). Explaining Explanations: An Overview of Interpretability of Machine Learning. Proceedings of the 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), Turin, Italy.","DOI":"10.1109\/DSAA.2018.00018"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3236009","article-title":"A Survey of Methods for Explaining Black Box Models","volume":"51","author":"Guidotti","year":"2019","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_38","unstructured":"Koza, J. (1992). Genetic Programming, MIT Press."},{"key":"ref_39","unstructured":"Steele, G. (1990). Common LISP: The Language, Digital Pr.. [2nd ed.]."},{"key":"ref_40","unstructured":"Bratko, I. (2012). Prolog Programming for Artificial Intelligence, Addison-Wesley. [4th ed.]."},{"key":"ref_41","unstructured":"Sellis, T.K., Miller, R.J., Kementsietsidis, A., and Velegrakis, Y. (2011, January 12\u201316). Datalog and emerging applications: An interactive tutorial. Proceedings of the ACM SIGMOD International Conference on Management of Data, Athens, Greece."},{"key":"ref_42","unstructured":"Thompson, S.J. (2011). Haskell\u2014The Craft of Functional Programming, Addison-Wesley. [3rd ed.]."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Gebser, M., Kaminski, R., Kaufmann, B., and Schaub, T. (2012). Answer Set Solving in Practice, Morgan & Claypool Publishers. Synthesis Lectures on Artificial Intelligence and Machine Learning.","DOI":"10.1007\/978-3-031-01561-8"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Lloyd, J.W. (1987). Foundations of Logic Programming, Springer. [2nd ed.].","DOI":"10.1007\/978-3-642-83189-8"},{"key":"ref_45","unstructured":"Arikawa, S., Goto, S., Ohsuga, S., and Yokomori, T. (1990, January 8\u201310). Inductive Logic Programming. Proceedings of the First International Workshop on Algorithmic Learning Theory, Tokyo, Japan."},{"key":"ref_46","first-page":"111","article-title":"Systematic search for lambda expressions","volume":"Volume 6","year":"2005","journal-title":"Revised Selected Papers from the Sixth Symposium on Trends in Functional Programming"},{"key":"ref_47","unstructured":"Law, M. (2018). Inductive Learning of Answer Set Programs. [Ph.D. Thesis, Imperial College London]."},{"key":"ref_48","unstructured":"Nezhad, A.T. (2013). Logic-Based Machine Learning Using a Bounded Hypothesis Space: The Lattice Structure, Refinement Operators and a Genetic Algorithm Approach. [Ph.D. Thesis, Imperial College London]."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1007\/s10994-013-5353-8","article-title":"Learning from interpretation transition","volume":"94","author":"Inoue","year":"2014","journal-title":"Mach. Learn."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"81","DOI":"10.3389\/fbioe.2014.00081","article-title":"Learning Delayed Influences of Biological Systems","volume":"2","author":"Ribeiro","year":"2015","journal-title":"Front. Bioeng. Biotechnol."},{"key":"ref_51","unstructured":"Mart\u00ednez Mart\u00ednez, D., Ribeiro, T., Inoue, K., Aleny\u00e0 Ribas, G., and Torras, C. (September, January 31). Learning probabilistic action models from interpretation transitions. Proceedings of the Technical Communications of the 31st International Conference on Logic Programming (ICLP 2015), Cork, Ireland."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Ribeiro, T., Magnin, M., Inoue, K., and Sakama, C. (2015, January 9\u201311). Learning Multi-valued Biological Models with Delayed Influence from Time-Series Observations. Proceedings of the 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA), Miami, FL, USA.","DOI":"10.1109\/ICMLA.2015.19"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Mart\u0131nez, D., Alenya, G., Torras, C., Ribeiro, T., and Inoue, K. (2016, January 12\u201317). Learning relational dynamics of stochastic domains for planning. Proceedings of the 26th International Conference on Automated Planning and Scheduling, London, UK.","DOI":"10.1609\/icaps.v26i1.13746"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Lachiche, N., and Vrain, C. (2018). Inductive Learning from State Transitions over Continuous Domains. Inductive Logic Programming, Springer.","DOI":"10.1007\/978-3-319-78090-0"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Ribeiro, T., Folschette, M., Magnin, M., Roux, O., and Inoue, K. (2018, January 2\u20134). Learning dynamics with synchronous, asynchronous and general semantics. Proceedings of the International Conference on Inductive Logic Programming, Ferrara, Italy.","DOI":"10.1007\/978-3-319-99960-9_8"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Ribeiro, T., Folschette, M., Magnin, M., and Inoue, K. (2021, November 03). Learning any Semantics for Dynamical Systems Represented by Logic Programs. Available online: https:\/\/hal.archives-ouvertes.fr\/hal-02925942\/.","DOI":"10.1007\/s10994-021-06105-4"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Ribeiro, T., and Inoue, K. (2015). Learning prime implicant conditions from interpretation transition. Inductive Logic Programming, Springer.","DOI":"10.1007\/978-3-319-23708-4_8"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/0304-3975(89)90126-6","article-title":"Paraconsistent logic programming","volume":"68","author":"Blair","year":"1989","journal-title":"Theor. Comput. Sci."},{"key":"ref_59","first-page":"45","article-title":"Paraconsistent foundations for logic programming","volume":"5","author":"Blair","year":"1988","journal-title":"J. Non-Class. Log."},{"key":"ref_60","unstructured":"Lhoussaine, C., and Remy, E. (2020). Les enjeux de l\u2019inf\u00e9rence de mod\u00e8les dynamiques des syst\u00e8mes biologiques \u00e0 partir de s\u00e9ries temporelles. Approches Symboliques de la Mod\u00e9lisation et de L\u2019analyse des Syst\u00e8mes Biologiques, ISTE Editions."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Ribeiro, T., Folschette, M., Magnin, M., and Inoue, K. (2021). Learning Any Memory-Less Discrete Semantics for Dynamical Systems Represented by Logic Programs. Mach. Learn., Available online: http:\/\/lr2020.iit.demokritos.gr\/online\/ribeiro.pdf.","DOI":"10.1007\/s10994-021-06105-4"},{"key":"ref_62","unstructured":"Iken, O., Folschette, M., and Ribeiro, T. (2021, January 25\u201327). Automatic Modeling of Dynamical Interactions Within Marine Ecosystems. Proceedings of the International Conference on Inductive Logic Programming, Online."},{"key":"ref_63","unstructured":"Kohavi, R. (1996, January 2\u20134). Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid. Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, Portland, OR, USA."},{"key":"ref_64","unstructured":"Fenner, S., and Fortnow, L. (2017). Compression Complexity. arXiv."},{"key":"ref_65","unstructured":"Varghese, D., and Tamaddoni-Nezhad, A. (July, January 29). One-Shot Rule Learning for Challenging Character Recognition. Proceedings of the 14th International Rule Challenge, Oslo, Norway."},{"key":"ref_66","unstructured":"Fierrez, J. (2006). Adapted Fusion Schemes for Multimodal Biometric Authentication. [Ph.D. Thesis, Universidad Politecnica de Madrid]."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.inffus.2017.12.003","article-title":"Multiple classifiers in biometrics. Part 1: Fundamentals and review","volume":"44","author":"Fierrez","year":"2018","journal-title":"Inf. Fusion"},{"key":"ref_68","unstructured":"Jajodia, S., Samarati, P., and Yung, M. (2021). Biometrics Security. Encyclopedia of Cryptography, Security and Privacy, Springer. Chapter Biometrics, Security."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"1038","DOI":"10.1109\/JSTSP.2020.3007250","article-title":"GANprintR: Improved Fakes and Evaluation of the State of the Art in Face Manipulation Detection","volume":"14","author":"Neves","year":"2020","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_70","unstructured":"Hernandez-Ortega, J., Daza, R., Morales, A., Fierrez, J., and Ortega-Garcia, J. (2020, January 7\u201312). edBB: Biometrics and Behavior for Assessing Remote Education. Proceedings of the AAAI Workshop on Artificial Intelligence for Education (AI4EDU), New York, NY, USA."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Gomez, L.F., Morales, A., Orozco-Arroyave, J.R., Daza, R., and Fierrez, J. (2021, January 19\u201325). Improving Parkinson Detection using Dynamic Features from Evoked Expressions in Video. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRw), Nashville, TN, USA.","DOI":"10.1109\/CVPRW53098.2021.00172"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"940","DOI":"10.1007\/s12559-020-09755-z","article-title":"Handwriting Biometrics: Applications and Future Trends in e-Security and e-Health","volume":"12","author":"Fierrez","year":"2020","journal-title":"Cogn. Comput."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Acien, A., Morales, A., Vera-Rodriguez, R., Fierrez, J., and Delgado, O. (2020, January 13\u201317). Smartphone Sensors For Modeling Human-Computer Interaction: General Outlook And Research Datasets For User Authentication. Proceedings of the IEEE Conference on Computers, Software, and Applications (COMPSAC), Madrid, Spain.","DOI":"10.1109\/COMPSAC48688.2020.00-81"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Tolosana, R., Ruiz-Garcia, J.C., Vera-Rodriguez, R., Herreros-Rodriguez, J., Romero-Tapiador, S., Morales, A., and Fierrez, J. (2021). Child-Computer Interaction: Recent Works, New Dataset, and Age Detection. arXiv.","DOI":"10.1109\/TETC.2022.3150836"}],"container-title":["Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-431X\/10\/11\/154\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:31:33Z","timestamp":1760167893000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-431X\/10\/11\/154"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,17]]},"references-count":74,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2021,11]]}},"alternative-id":["computers10110154"],"URL":"https:\/\/doi.org\/10.3390\/computers10110154","relation":{},"ISSN":["2073-431X"],"issn-type":[{"value":"2073-431X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,17]]}}}