A Comprehensive Database for Benchmarking Imaging Systems

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 alg...

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Vydáno v:IEEE transactions on pattern analysis and machine intelligence Ročník 42; číslo 3; s. 509 - 520
Hlavní autoři: Panetta, Karen, Wan, Qianwen, Agaian, Sos, Rajeev, Srijith, Kamath, Shreyas, Rajendran, Rahul, Rao, Shishir Paramathma, Kaszowska, Aleksandra, Taylor, Holly A., Samani, Arash, Yuan, Xin
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.03.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Adolescent
Adult
Aged
Algorithms
Automated Facial Recognition - methods
Benchmarking
Cameras
Child
Child, Preschool
Colleges & universities
computerized face sketches
cross-modality
Databases, Factual
Face
Face - anatomy & histology
Face - diagnostic imaging
Face recognition
Female
Humans
Image acquisition
Image Processing, Computer-Assisted - methods
Image recognition
Imaging, Three-Dimensional
Infrared imagery
Infrared photography
Machine learning
Male
Middle Aged
near infrared
The tufts face database
thermal
Three-dimensional displays
Young Adult
ISSN:0162-8828, 1939-3539, 2160-9292, 1939-3539
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Popis
Shrnutí: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.
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ISSN:0162-8828
1939-3539
2160-9292
1939-3539
DOI:10.1109/TPAMI.2018.2884458