Using Real-time Facial Recognition for Vehicle Driver Authentication

使用实时面部识别进行车辆驾驶员身份验证

• 批准号: 537221-2018
• 负责人: Etemad, Ali
• 金额: $ 2.91万
• 依托单位: Queen's University
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=720262
• 关键词: Using; Real; time; Facial; Recognition

Smart vehicles are rapidly becoming a reality with advances in sensor technologies, machine learning, processing power, and internet access. With advances and popularization of intelligent devices and environments such as vehicles, it is increasingly critical that security and authentication be robust and reliable. Facial recognition for authentication of drivers is a real possibility that needs to be further studied, fine-tuned, and tested. In this project, we aim to tackle a number of important research questions associated with facial recognition for driver authentication in smart vehicles. These questions include i) identifying the minimum number, orientation, and setup of cameras required to reliably and robustly perform the task at hand, ii) developing robust models capable of recognizing and authenticating human faces with minimal training samples, under challenging conditions, and with changes over time, iii) identifying the optimum time for re-identification (short-term) as well as re-training (long-term), and iv) how users feel about and interact with this kind of technology. The project will train a postdoctoral fellow, a PhD student, and two master's students. The main beneficiary of this work will be Irdeto Canada, the Canadian arm of an international software and security company. The company will take immediate steps towards productization and commercialization of the technology with current customers having already shown interest. Additionally, in recent years, Canada has emerged as a global leader in artificial intelligence and machine learning, which has already resulted in significant economic benefits and growth. This project will contribute to Canada's AI ecosystem through development of novel algorithms and intelligent systems in the fields of image processing and security.

随着传感器技术、机器学习、处理能力和互联网接入的进步，智能汽车正迅速成为现实。随着车辆等智能设备和智能环境的进步和普及，安全性和身份验证的鲁棒性和可靠性变得越来越重要。面部识别用于驾驶员身份验证是一种真正的可能性，需要进一步研究、微调和测试。在这个项目中，我们的目标是解决一些与智能车辆驾驶员身份验证面部识别相关的重要研究问题。这些问题包括：1)确定可靠、稳健地执行手头任务所需的最小数量、方向和摄像机设置；2)在具有挑战性的条件下，随着时间的推移，开发能够用最少训练样本识别和验证人脸的稳健模型；3)确定重新识别（短期）和重新训练（长期）的最佳时间。iv)用户对这种技术的感受和交互方式。该项目将培养1名博士后、1名博士生和2名硕士生。这项工作的主要受益者将是一家国际软件和安全公司的加拿大分公司爱迪德加拿大公司。该公司将立即采取措施，实现该技术的产品化和商业化，目前的客户已经表现出兴趣。此外，近年来，加拿大已成为人工智能和机器学习领域的全球领导者，这已经带来了显著的经济效益和增长。该项目将通过开发图像处理和安全领域的新算法和智能系统，为加拿大的人工智能生态系统做出贡献。