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.

中文翻译：

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用于基准成像系统的综合数据库


由于不同传感器在日常生活应用中的广泛使用，跨模态人脸识别是一个新兴主题。人脸识别系统的开发很大程度上依赖于现有数据库来评估和获取数据密集型机器学习算法的训练示例。然而，目前还没有公开的人脸数据库包含同一主题的两种以上模式。在这项工作中，我们介绍了塔夫茨人脸数据库，其中包括以各种方式获取的图像：照片图像、热图像、近红外图像、录制的视频、计算机化的面部草图以及每个志愿者面部的 3D 图像。获得了机构研究委员会协议，并从塔夫茨大学的学生、教职员工、教师及其家人收集了图像。该数据库包含来自超过 15 个不同国家、不同性别身份、年龄和种族背景的 113 个人的 10,000 多张图像。这项工作的贡献是：1）详细描述了塔夫茨人脸数据库中图像的内容和获取过程； 2) 塔夫茨人脸数据库向全世界的研究人员开放，这将允许评估和创建更强大、一致和适应性更强的识别算法； 3）对人脸识别系统和人脸数据集进行全面、最新的审查。