Robust Algorithms for Real-World Face Recognition

用于现实世界人脸识别的稳健算法

• 批准号: 514500-2017
• 负责人: Gong, Minglun
• 金额: $ 1.82万
• 依托单位: Memorial University of Newfoundland
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=639304
• 关键词: Robust; Algorithms; Real; World; Face

A practical face verification system is expected to achieve a False Accept Rate of 0.01% or lower whileensuring a False Reject Rate (FRR) of 5% or lower. In recent years, face recognition algorithms based on deepneural networks have achieved high accuracy when tested on face recognition database. However, when putinto real-world application, those algorithms are often not robust enough, due to factors such as differentlighting conditions, camera distances, face orientations, and occlusions.As a startup company, AltumView Systems Inc. has been developing various intelligent camera products sinceJanuary 2015. In this project, the university team will work with AltumView to improve the performance androbustness of its existing face recognition products. Research to be conducted includes: 1) try differentconfigurations of the cost functions of the deep neural network scheme, and identify the most effective way; 2)design new neural network architecture that is robust to different lighting conditions, camera distances, andface angles etc. between the ID photos and wild input photos; 3) design schemes to deal with partially occludedfaces caused by e.g., glasses; 4) investigate Generative Adversarial Networks for preprocessing of input photosand domain adaptation; and 5) investigate how to combine different techniques to improve the overallperformance of the system.

一个实用的人脸验证系统，期望达到0.01%或更低的错误接受率，同时确保5%或更低的错误识别率（FRR）。近年来，基于深度神经网络的人脸识别算法在人脸识别数据库上进行测试时，取得了很高的准确率。然而，在实际应用中，由于光照条件、摄像机距离、人脸方向、遮挡等因素的影响，这些算法往往不够鲁棒。自2015年1月以来一直在开发各种智能摄像机产品。在这个项目中，大学团队将与AltumView合作，以提高其现有人脸识别产品的性能。要进行的研究包括：1）尝试深度神经网络方案的成本函数的不同配置，并确定最有效的方式; 2）设计新的神经网络架构，该架构对ID照片和野生输入照片之间的不同光照条件，相机距离和面部角度等具有鲁棒性; 3）设计方案来处理由例如，眼镜; 4）研究用于输入照片预处理和域自适应的生成对抗网络;以及5）研究如何联合收割机结合不同技术来提高系统的整体性能。