A Comprehensive Database for Benchmarking Imaging Systems
| Autor: | Rahul Rajendran, Holly A. Taylor, Arash Samani, Karen Panetta, Sos S. Agaian, Qianwen Wan, Srijith Rajeev, Xin Yuan, Aleksandra Kaszowska, Shishir Paramathma Rao, K M Shreyas Kamath |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Adult
Male Adolescent Databases Factual Computer science Automated Facial Recognition ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Image processing 02 engineering and technology computer.software_genre Facial recognition system Young Adult Imaging Three-Dimensional Artificial Intelligence 0202 electrical engineering electronic engineering information engineering Image Processing Computer-Assisted Humans Child Protocol (object-oriented programming) Aged Database Applied Mathematics Benchmarking Middle Aged Sketch Computational Theory and Mathematics Face (geometry) Child Preschool Face 020201 artificial intelligence & image processing Female Computer Vision and Pattern Recognition computer Software Algorithms |
| Zdroj: | IEEE transactions on pattern analysis and machine intelligence. 42(3) |
| ISSN: | 1939-3539 |
| Popis: | Cross-modality face recognition is an emerging topic due to the wide-spread usage of different sensors in day-to-day life applications. The development of face recognition systems relies greatly on existing databases for evaluation and obtaining training examples for data-hungry machine learning algorithms. However, currently, there is no publicly available face database that includes more than two modalities for the same subject. In this work, we introduce the Tufts Face Database that includes images acquired in various modalities: photograph images, thermal images, near infrared images, a recorded video, a computerized facial sketch, and 3D images of each volunteer's face. An Institutional Research Board protocol was obtained and images were collected from students, staff, faculty, and their family members at Tufts University. The database includes over 10,000 images from 113 individuals from more than 15 different countries, various gender identities, ages, and ethnic backgrounds. The contributions of this work are: 1) Detailed description of the content and acquisition procedure for images in the Tufts Face Database; 2) The Tufts Face Database is publicly available to researchers worldwide, which will allow assessment and creation of more robust, consistent, and adaptable recognition algorithms; 3) A comprehensive, up-to-date review on face recognition systems and face datasets. |
| Databáze: | OpenAIRE |
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