Adaptive Context-Based Systems for Face Recognition in Video Surveillance

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。 该研究计划的主要目标是研究和开发新的静止到视频FR系统，用于跨多个摄像机对感兴趣的个体进行准确的时空识别。为了补偿注册期间可用的有限数量的标记参考静止图像，系统的面部模型将基于来自其操作域（Od）的上下文信息进行调整，以维持高水平的性能。特别是，这项研究依赖于一个银行的未标记（非目标）的视频轨迹从每个不同的摄像头在监控网络。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的捕获条件的分布进行建模，（2）使用用于域自适应的基于实例和特征的方法来为每个登记的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）执行由捕获条件的变化驱动的检测器的有效自更新。该计划按照3个轴组织： 1.从Od轨迹模拟捕获条件密度。 2.示例支持向量机的动态集成，利用域自适应来精确检测个体。将开发新的技术来评估分类器的能力，自我更新，并生成合成剧照。 3.扩展稀疏表示分类器，使用域自适应来准确检测个体。新的技术将被开发来压缩，综合生成，并专门字典到一个Od。 该计划开发的创新技术将有助于观察列表筛选以及其他几种视觉监控和识别应用的上下文敏感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。虽然政府、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HQP和鼓励未来的拨款申请提供极好的机会。

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.