Adaptive Context-Based Systems for Face Recognition in Video Surveillance

视频监控中基于上下文的自适应人脸识别系统

• 批准号: RGPIN-2016-06783
• 负责人: Granger, Eric
• 金额: $ 1.89万
• 依托单位: École de technologie supérieure
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 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=634676
• 关键词: Adaptive; Context; Based; Systems; Face

Still-to-video face recognition (FR) is an important function in video surveillance, where faces captured across a network of video cameras are matched against reference stills images. Currently, the performance of state-of-the-art systems is severely affected by the multiple non-stationary operational environments, defined by variations in, e.g., pose, illumination and camera viewpoint. Moreover, since an individual is typically enrolled using one or few reference stills captured during enrolment, face models are often poor representatives of the faces to be recognized during operations.

静止到视频人脸识别（FR）是视频监控中的重要功能，其中通过视频摄像机网络捕获的人脸与参考静止图像进行匹配。目前，现有技术系统的性能受到多个非平稳操作环境的严重影响，所述多个非平稳操作环境由例如姿势、照明和相机视点。此外，由于通常使用在登记期间捕获的一个或几个参考静止图像来登记个体，因此面部模型通常是要在操作期间识别的面部的差的代表。