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

该项目旨在通过对不同特征进行建模，改变研究人员处理人脸识别技术的方式。人类能够识别熟悉面孔的图像，即使它们变得极其扭曲。该项目寻求通过模仿人类感知来模拟人脸识别技术，以解决当前的问题。该项目通过将自动人脸识别和人脸感知领域的研究结合起来，产生人脸记忆的计算模型，从而推动了这两个领域的发展。这项研究将通过生产能够识别低质量图像中的人脸的技术来造福社会，就像在监控和人机交互环境中看到的那样。该项目通过招募多样化的研究团队、将研究成果纳入人工智能课程以及广泛传播研究成果、数据和代码来支持教育和多样性。该项目由稳健情报(RI)、已建立的激励竞争研究计划(EPSCoR)以及安全和值得信赖的网络空间(SATC)计划联合资助。这项研究调查了是否可以通过识别和强调不同的面部特征来提高自动人脸验证的性能。该项目专注于三个主要目标：1)建立明显的面部特征模型，2)使用显著特征进行人脸验证，3)建立夸张的显著特征模型。在对不同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，每个身份都标有不同的特征。将制定识别独特特征的基线和强有力的方法，并与数据一起公开提供。对于使用不同特征的人脸验证，将在几个大规模监控和人机交互数据集上探索和评估多任务学习方法。对夸张的人脸特征进行建模的方法包括从弱标签数据学习生成模型，以从真实人脸生成逼真的人脸图像。自动生成的图像将用于打破端到端深度学习框架，用于低质量图像的人脸验证。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.