Simulation of atmospheric turbulence effects and mitigation algorithms on stand-off automatic facial recognition
Simulation of atmospheric turbulence effects and mitigation algorithms on stand-off automatic facial recognition
复制标题
大气湍流效应模拟及远距离自动面部识别缓解算法
DOI：
10.1117/12.979480
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发表时间：
2012
影响因子：
3.9
通讯作者：
D. Oxford
中科院分区：
文献类型：
--
作者：
Kevin R. Leonard;J. Howe;D. Oxford
对峙基地和兵力保护监视措施主要依靠光电和热成像技术。大气湍流会导致模糊、失真和强度波动，这可能会严重降低这些系统的图像质量。这项工作探索了湍流图像退化对自动人脸识别软件性能的影响，并着眼于湍流缓解算法的潜在好处。这项工作的目标是了解在退化成像条件下进行远程人脸识别的可行性。为了创建足够大的数据库以进行匹配，使用水平视图湍流模拟器和面部识别技术(FERET)数据库的子集创建了不同范围和湍流条件的模拟图像。然后用人脸识别软件对模拟的湍流退化图像进行处理，并将结果与原始图像集的结果进行比较。最后，给出了抗湍流图像人脸识别软件的性能。
Stand-off base and force protection surveillance measures primarily rely on electro-optic and thermal imaging technology. Atmospheric turbulence causes blur, distortion and intensity fluctuations that can severely degrade the image quality of these systems. This work explores the effects of turbulence image degradation on the performance of automatic facial recognition software and also looks at the potential benefit of turbulence mitigation algorithms. The goal of this work is to understand the feasibility of long-range facial recognition in degraded imaging conditions. In order to create a large enough database to match against, simulated imagery of different ranges and turbulence conditions were created using a horizontal view turbulence simulator and a subset of the Facial Recognition Technology (FERET) database. The simulated turbulence degraded imagery was then processed with facial recognition software and the results are compared against those from the pristine image set. Finally, the performance of the facial recognition software with turbulence mitigated imagery is also presented.