Repositori institucional URV
| URV's Author/s: | Domingo Ferrer, Josep / Sánchez Ruenes, David |
| Author, as appears in the article.: | Haffar, Rami; Sanchez, David; Domingo-Ferrer, Josep |
| Author's mail: | josep.domingo@urv.cat david.sanchez@urv.cat |
| Author identifier: | 0000-0001-7213-4962 0000-0001-7275-7887 |
| Journal publication year: | 2025 |
| Publication Type: | Journal Publications |
| APA: | Haffar, Rami; Sanchez, David; Domingo-Ferrer, Josep (2025). Multi-Task Faces (MTF) Data Set: A Legally and Ethically Compliant Collection of Face Images for Various Classification Tasks. Ieee Access, 13(), 63827-63840. DOI: 10.1109/ACCESS.2025.3559310 |
| Paper original source: | Ieee Access. 13 63827-63840 |
| Abstract: | Human facial data offers valuable potential for tackling classification problems, including face recognition, age estimation, gender identification, emotion analysis, and race classification. However, recent privacy regulations, particularly the EU General Data Protection Regulation, have restricted the collection and usage of human images in research. As a result, several previously published face data sets have been removed from the internet due to inadequate data collection methods and privacy concerns. While synthetic data sets have been suggested as an alternative, they fall short of accurately representing the real data distribution. Additionally, most existing data sets are labeled for just a single task, which limits their versatility. To address these limitations, we introduce the Multi-Task Face (MTF) data set, designed for various tasks including face recognition and classification by race, gender, and age, as well as for aiding in training generative networks. The MTF data set comes in two versions: a non-curated set containing 132,816 images of 640 individuals, and a manually curated set with 5,246 images of 240 individuals, meticulously selected to maximize their classification quality. Both data sets were ethically sourced, using publicly available celebrity images in full compliance with copyright regulations. Along with providing detailed descriptions of data collection and processing, we evaluated the effectiveness of the MTF data set in training five deep learning models across the aforementioned classification tasks, achieving up to 98.88% accuracy for gender classification, 95.77% for race classification, 97.60% for age classification, and 79.87% for face recognition with the ConvNeXT model. Both MTF data sets can be accessed through the following link. https://github.com/RamiHaf/MTF_data_set |
| Article's DOI: | 10.1109/ACCESS.2025.3559310 |
| Link to the original source: | https://ieeexplore.ieee.org/document/10960301 |
| Paper version: | info:eu-repo/semantics/publishedVersion |
| licence for use: | https://creativecommons.org/licenses/by/3.0/es/ |
| Department: | Enginyeria Informàtica i Matemàtiques |
| Licence document URL: | https://repositori.urv.cat/ca/proteccio-de-dades/ |
| Thematic Areas: | Ciência da computação Computer science (all) Computer science (miscellaneous) Computer science, information systems Electrical and electronic engineering Engenharias iii Engenharias iv Engineering (all) Engineering (miscellaneous) Engineering, electrical & electronic General computer science General engineering General materials science Materials science (all) Materials science (miscellaneous) Telecommunications |
| Keywords: | Artificial intelligence Data models Deep learnin Deep learning Ethics Europe Face images Face recognition Faces General data protection regulation Image classification Image data set Law Regulation Training |
| Entity: | Universitat Rovira i Virgili |
| Record's date: | 2025-04-30 |
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