TY - JOUR
T1 - A Comprehensive Database for Benchmarking Imaging Systems
AU - Panetta, Karen
AU - Samani, Arash
AU - Yuan, Xin
AU - Wan, Qianwen
AU - Agaian, Sos
AU - Rajeev, Srijith
AU - Kamath, Shreyas
AU - Rajendran, Rahul
AU - Rao, Shishir Paramathma
AU - Kaszowska, Aleksandra
AU - Taylor, Holly A.
N1 - Publisher Copyright:
© 1979-2012 IEEE.
PY - 2020/3/1
Y1 - 2020/3/1
N2 - 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.
AB - 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.
KW - 3D
KW - computerized face sketches
KW - face recognition, cross-modality
KW - near infrared
KW - The tufts face database
KW - thermal
UR - http://www.scopus.com/inward/record.url?scp=85057884629&partnerID=8YFLogxK
U2 - 10.1109/TPAMI.2018.2884458
DO - 10.1109/TPAMI.2018.2884458
M3 - Journal article
C2 - 30507525
AN - SCOPUS:85057884629
SN - 0162-8828
VL - 42
SP - 509
EP - 520
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
JF - IEEE Transactions on Pattern Analysis and Machine Intelligence
IS - 3
M1 - 8554155
ER -