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: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=616513
• 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.

静态视频人脸识别 (FR) 是视频监控中的一项重要功能，其中通过摄像机网络捕获的人脸与参考静态图像进行匹配。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，这些操作环境是由姿态、照明和相机视点等变化定义的。此外，由于通常使用在注册期间捕获的一张或几张参考静止图像来注册个人，因此面部模型通常不能很好地代表操作期间要识别的面部。 该研究计划的主要目标是研究和开发新的静态到视频 FR 系统，以便跨多个摄像机准确地时空识别感兴趣的个体。为了弥补注册期间可用的标记参考静态图像数量有限的问题，系统的面部模型将根据其操作域 (Od) 的上下文信息进行调整，以维持高水平的性能。特别是，这项研究依赖于监控网络中每个不同摄像机的一组未标记（非目标）视频轨迹。对于每个 Od，这些系统将允许：（1）根据特征和 ROI 质量空间对面部轨迹中捕获条件的分布进行建模，（2）使用基于实例和基于特征的域适应方法为每个注册个体生成准确的检测器，（3）生成合成参考 ROI，（4）根据捕获条件对每个检测器的响应进行加权和积分，以及（5）执行由捕获变化驱动的检测器的有效自我更新 状况。该程序按照 3 个轴组织： 1. 根据 Od 轨迹对捕获条件密度进行建模。 2. 示例 SVM 的动态集成，利用域适应来准确检测个体。将开发新技术来评估分类器能力、自我更新和生成合成剧照。 3. 扩展稀疏表示分类器，使用域适应来准确检测个体。将开发新技术来压缩、综合生成和专门化字典到 Od。 该计划中开发的创新技术将有助于用于监视列表筛选以及其他几种视觉监控和识别应用的上下文相关 FR 技术的出现。因此，这项研究将在模式识别、进化计算和计算机视觉领域保持相关性。尽管政府、军队和执法部门仍然是视频监控技术的主要用户，但最近出现的许多商业应用和消费设备也将为与公司合作、培训总部和鼓励未来的资助申请提供绝佳的机会。