UMAFAE · Unaccompanied Minors Automatic Forensic Age Estimation
„Хоризонт 2020“ — Действия „Мария Склодовска-Кюри“
- Период
- 2022-03-01 → 2024-02-29
- Финансиране от ЕС
- 172 932 €
- Участници
- 1
- Схема
- MSCA-IF
Линиите свързват координатора с партньорите.
Накратко на български
Алгоритми с изкуствен интелект анализират рентгенови снимки, за да определят възрастта на несъпроводени непълнолетни мигранти. Това помага за установяване на идентичността на хора без документи, за да получат правата, които им се полагат по закон.
Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.
Резултати накратко
Unaccompanied Minors Automatic Forensic Age Estimation
UMAFAE’s goal is to develop Artificial Intelligence (AI)-based algorithms that estimate the age of a migrant minor without any family reference by analysing radiographic images. Age is an innate characteristic of our own identity. However, in many countries, especially on the African continent, less than 10% of new births have been reported in a civil registry, in contrast to the official rate in European countries (90%). For this reason, applicants for international protection may arrive in the EU undocumented, or with unreliable identity documents. As a more serious consequence, these minors may end up deprived of the rights to which they are entitled. Therefore, UMAFAE is of extreme relevance due to its social and legal implications. It provides a key contribution to the migration challenge through more efficient systems that will support forensic experts in the estimation process, contributing to the “Inclusive, Innovative and Thoughtful Societies” challenge. Additionally, UMAFAE is completely aligned with the European Union’s 17 sustainable development goals, specifically, Goal 16 (Peace, Justice and Strong Institutions), since it promotes inclusive societies and build effective and capable institutions at all levels. The specific objectives are as follows: 1) to improve the accuracy of current age estimation methods in minors by means of AI-based techniques; 2) to develop fully automated methods; 3) to shift from current observational methods to objective, fast, accurate, robust and reproducible ones; 4) to develop models that are explainable and interpretable for the forensic community, especially for the courts of justice, responsible for the decision-making process; 5) to validate the models on real cases of migrant minors without family references; and 6) to contribute to the EU’s action and agenda to protect the rights of minors. Regarding point 5, carrying out the validation on real cases of undocumented migrant minors, whose files are stored in the most important institutes of forensic medicine and forensic sciences in Spain, is a necessary step to demonstrate the validity of the data, the robustness of the algorithms and the reliability of the process itself. The collaboration of the indicated institutions allows us to close a cycle, and to find out whether forensic professionals will really be able to apply these models in their forensic work, according to sound statistical standards and the most important principles of child protection.
Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз
Цел на проекта
In recent years, the European Union is facing an unprecedented wave of mass migration and governments and institutions are struggling to keep up. One particular difficult topic is the age estimation of unaccompanied minors: in 2017 there were more than 20.000. Around 90% of them were between 15 and 17 years old and most of them were undocumented.Several scientific publications are addressing the issue of age estimation. The focus is usually a specific anatomical region or a set of them. Regions are then analyzed within different medical image modalities to provide an age estimation. However, the limitations of state-of-the-art methods are as follows: i) subjectivity and lack of sound validation tests; ii) the majority is error-prone and time-consuming; iii) limited sample size; iv) vast population-specific differences in the patterns of maturation, and v) lack of reliability and reproducibility. UMAFAE’s goal is to develop AI-based algorithms that determine the age of a person. This challenging proposal is achievable due to the ER (Dr. De Luca) and supervisor (Dr. Ibáñez) profiles. The ER is one of the most recognized European experts for age estimation, with a vast experience in forensic practice. Dr. Ibáñez is a world pioneer in the application of Artificial Intelligence techniques to Forensic Anthropology, with the largest number of publications and patents in this multidisciplinary field. The know-how of Panacea’s research team in different fields (from forensic and medical imaging to deep learning and computer vision), the equipment and facilities of the University of Granada (UMAFAE’s partner), together with a large list of data providers (agreements with six Universities) and the support of the main leading international experts in the field (Drs. Cameriere, Schmeling, Viner, Márquez-Grant and Garamendi) compose a unique environment to accomplish these ambitious and groundbreaking objectives. The project includes a clear plan for technology transfer.
Оригинален текст от CORDIS (на английски).
Участници
- PANACEA COOPERATIVE RESEARCH S COOP · PonferradaКоординаторИспания
Връзки
- Виж в CORDIS
- DOI: 10.3030/101026482
- https://panacea-coop.com/projects/unaccompanied-minors-automatic-forensic-ageestimation-umafae/
Данни: CORDIS, © Европейски съюз
