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: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=709270
• 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. The main objective of this research program is to investigate and develop new still-to-video FR systems for accurate spatio-temporal recognition of individuals of interest across multiple cameras. To compensate for the limited number of labeled reference stills available during enrolment, the system's facial models will adapt based on contextual information from its operational domain (Od) in order to sustain a high level of performance. In particular, this research relies on a bank of unlabeled (non-target) video trajectories from each different camera in a surveillance network. For each Od, this these systems will allow to: (1) model the distribution of capture conditions in facial trajectories according to feature and ROI quality spaces, (2) generate accurate detectors for each enrolled individual using instance- and feature-based methods for domain adaptation, (3) generate synthetic reference ROIs, (4) weight and integrate the response of each detector according to capture condition, and (5) perform efficient self-update of detectors driven by changes in capture conditions. This program is organized according to 3 axes: 1. Modeling the capture conditions densities from Od trajectories. 2. Dynamic ensembles of exemplar SVMs that exploit domain adaptation for accurate detection of individuals. New techniques will be developed to assess classifier competence, self-update, and generate synthetic stills. 3. Extended sparse representation classifiers that use domain adaptation for accurate detection of individuals. New techniques will be developed to compress, synthetically generate, and specialize dictionaries to an Od. Innovative techniques developed in this program will contribute to the emergence of context-sensitive FR technologies for watchlist screening, and for several other visual monitoring and recognition applications. As such, this research will remain relevant in the areas of pattern recognition, evolutionary computing and computer vision. Although government, military and law enforcement remain the principal users of video surveillance technologies, the recent emergence of many commercial applications and consumer devices will also offer excellent opportunities for partnering with companies, training HQP, and encourage future grant applications.

静止到视频的人脸识别（FR）是视频监控中的一项重要功能，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，最先进系统的性能受到多种非固定操作环境的严重影响，这些环境由姿势、照明和相机视点等变化所定义。此外，由于个体通常使用在登记过程中捕获的一个或几个参考静止图像进行登记，因此面部模型通常不能代表在操作中要识别的面部。