A Comprehensive Database for Benchmarking Imaging Systems

• NCJ Number: 306595
• Journal: IEEE Transactions on Pattern Analysis and Machine Intelligence Volume: 42 Issue: 3 Dated: March 2020 Pages: 509-520
• Date Published: March 2020
• Author(s): Karen Panetta; Qianwen Wan; Sos Agaian; Srijith Rajeev; Shreyas Kamath; Rahul Rajendran; Shishir Paramathma Rao; Aleksandra Kaszowska; Holly A. Taylor; Arash Samani; Xin Yuan
• Length: 12 pages
• Grant Number(s): 2019-R2-CX-0036
• Agencies: NIJ-Sponsored
• Publication Type: Research (Applied/Empirical)

Annotation

This article presents the Tufts Face Database as a prototype facial recognition resource that includes images acquired in multiple modalities (photograph images, thermal images, near infrared images, a recorded video, a computerized facial sketch, and 3D images) as an improvement over existing facial recognition databases that only incorporate two modalities.

Abstract

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, the authors 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. (Published Abstract Provided)