I-Corps: Low False Negative 3-D Facial Recognition

I-Corps：低误报 3D 面部识别

• 批准号: 1830782
• 负责人: Thomas Sterling
• 金额: $ 5万
• 依托单位: Indiana University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-04-01 至 2019-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1830782&HistoricalAwards=false
• 关键词: Corps; Low; False; Negative; Facial

The broader impact/commercial potential of this I-Corps project is the expansion of facial recognition technologies into the emissive infrared bands and the reduction of false negatives in the presence of large facial pose variation. Visible 2-D image-based machine learning techniques dominate the commercial facial recognition landscape while heterogeneous facial recognition techniques including 3-D to 2-D matching and infrared to visible matching have not seen similar widespread commercial deployment. The patented technology originates from an NSF Award and brings a crucial algorithmic component for commercial deployment that enhances heterogeneous facial recognition techniques while not relying on machine learning nor its concomitant large training database requirements. This in turn opens up the potential for improved automated surveillance against a given watch-list deployable in a host of public and private spaces and under a wider range of surveillance conditions than currently possible using 2-D commercial matching algorithms.This I-Corps project is a result of technology developed under an NSF Award which enables a radical improvement in 3-D facial recognition by reducing the computational costs for facial implicit surface generation and biometric capture originating from multispectral (visible and infrared) sources. The technology results in robust recognition in the presence of large facial pose variation and occlusion using 3-D facial implicit surfaces. The improvement over existing 2-D commercial facial recognition algorithms is evident under conditions typical of automated surveillance where large facial pose variation and poor lighting result in false negatives using conventional practice. The method and apparatus for 3-D facial recognition underlying this research, however, performs equally well under large facial pose variation and is able to seamlessly integrate multiple band infrared input along with visible image input for vastly lower false negative facial recognition even in the presence of low-light and nighttime conditions.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这个I-Corps项目的更广泛的影响/商业潜力是将面部识别技术扩展到发射红外波段，并在存在较大面部姿势变化的情况下减少假阴性。 可见的基于2-D图像的机器学习技术主导了商业面部识别领域，而包括3-D到2-D匹配和红外到可见光匹配的异构面部识别技术还没有看到类似的广泛商业部署。 该专利技术源于NSF奖，为商业部署带来了关键的算法组件，可增强异构面部识别技术，同时不依赖于机器学习，也不依赖于随之而来的大型训练数据库要求。 这反过来又打开了针对给定的监视列表进行改进的自动化监视的潜力，该监视列表可部署在许多公共和私人空间中，并且在比目前使用2-D商业匹配算法更广泛的监视条件下进行。通过减少面部隐式表面生成和源自多光谱（可见光和红外）源的生物特征捕获的计算成本来实现3D面部识别。 该技术使用3-D面部隐式表面在存在大的面部姿势变化和遮挡的情况下产生鲁棒的识别。 在自动化监控的典型条件下，现有的2-D商业面部识别算法的改进是显而易见的，其中大的面部姿势变化和较差的照明导致使用常规实践的假阴性。 然而，作为这项研究基础的三维面部识别方法和设备，在大的面部姿态变化下同样表现良好，并且能够将多波段红外输入沿着可见图像输入无缝地集成，以大大降低假阴性面部识别，该奖项反映了NSF的法定使命，并通过使用基金会的知识产权进行评估，优点和更广泛的影响审查标准。