RI: Small: Toward Human-Level Face Verification Performance Using Distinctive Features

RI：小：利用独特特征实现人类水平的人脸验证性能

基本信息

批准号：
1909707
负责人：
Emily Hand
金额：
$ 42万
依托单位：
Board of Regents, NSHE, obo University of Nevada, Reno
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-08-01 至 2024-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1909707&HistoricalAwards=false
关键词：
RI Small Toward Human Level

项目摘要

This project aims to transform the way in which researchers approach face recognition technology by modeling distinctive features. Humans are capable of recognizing images of familiar faces even as they become extremely distorted. This project seeks to address current issues with face recognition technology by modeling the process after human perception. This project advances the fields of automated face recognition and human face perception by combining research in both areas to produce computational models of human face memory. This research will benefit society by producing techniques capable of recognizing faces in low-quality imagery as seen in surveillance and human-computer interaction settings. This project supports education and diversity through the recruitment of a diverse research team, the incorporation of research results into artificial intelligence courses and the wide dissemination of research results, data and code. This project is jointly funded by the Robust Intelligence (RI), the Established Program to Stimulate Competitive Research (EPSCoR), and the Secure and Trustworthy Cyberspace (SaTC) programs. This research investigates whether automated face verification performance can be improved by recognizing and emphasizing distinctive facial features. The project focuses on three main objectives: 1) modeling distinctive facial features, 2) face verification using distinctive features and 3) modeling exaggerated distinctive features. In modeling distinctive facial features, a new set of data will be collected with many images per identity and each identity labeled with distinctive features. Baseline and robust approaches to distinctive feature recognition will be developed and made publicly available along with the data. For face verification using distinctive features, multi-task learning approaches will be explored and evaluated on several large-scale surveillance and human-computer interaction datasets. The approach for modeling exaggerated distinctive features of faces involves learning generative models from weakly labeled data to produce realistic facial images from veridical faces. Automatically generated images will then be used to break up end-to-end deep learning frameworks for face verification in low-quality imagery.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.

该项目旨在通过建模独特的特征来改变研究人员面对识别技术的方式。人类能够识别熟悉的面孔的图像，即使它们变得非常扭曲。该项目旨在通过对人类感知后的过程进行建模，以解决面部识别技术的当前问题。该项目通过结合两个领域的研究来产生人体面部记忆的计算模型，从而提高了自动面部识别和人脸知觉领域。这项研究将通过生产能够在监视和人机交互环境中识别出低质量图像中面孔的技术来使社会受益。该项目通过招募多样化的研究团队，将研究结果纳入人工智能课程以及广泛传播研究结果，数据和代码来支持教育和多样性。该项目由稳健情报（RI），既定的竞争研究（EPSCOR）以及安全且可信赖的网络空间（SATC）计划共同资助。这项研究调查了是否可以通过识别和强调独特的面部特征来提高自动面部验证性能。该项目的重点是三个主要目标：1）建模独特的面部特征，2）使用独特功能的面部验证和3）建模夸张的独特特征。在对独特的面部特征进行建模时，将收集一组新的数据，每个身份都有许多图像，并标记为具有独特功能的每个标识。将开发并与数据一起开发并公开提供独特特征识别的方法。为了使用独特的功能进行面部验证，将在几个大型监视和人机交互数据集上探索和评估多任务学习方法。建模的方法夸大了面部的独特特征，涉及从弱标记的数据中学习生成模型，以从垂直面孔产生逼真的面部图像。然后，将使用自动生成的图像来分解端到端的深度学习框架，以进行低质量图像中的面部验证。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的智力优点和更广泛影响的评估评估标准的评估值得支持的。