基于局部特征的身份推理中分辨率失配的动态改善
Dynamic Amelioration of Resolution Mismatches for Local Feature Based Identity Inference
摘要：
虽然现有的基于局部特征的人脸识别系统对诸如未对准等问题具有鲁棒性，但当比较不同分辨率的图像时，它们可能会表现出准确性下降。这在监控环境中很常见，其中将高分辨率的面部照片库与低分辨率的CCTV探头图像进行比较，或者给定图像的大小不是潜在分辨率的可靠指标（例如，光学性能差）。为了减轻这种退化，我们提出了一个补偿框架，动态选择最合适的人脸识别系统，为一对给定的图像分辨率。该框架应用了一种新的分辨率检测方法，该方法不依赖于输入图像的大小，而是利用局部特征对分辨率的敏感性，使用概率多区域直方图方法。实验的分辨率修改版本的“标签的脸在野外”数据集显示，建议的分辨率检测器前端获得99%的平均准确率在选择最合适的人脸识别系统，从而导致更高的整体人脸识别精度（在几个分辨率）相比，个别基线人脸识别系统。
英文摘要：
While existing face recognition systems based on local features are robust to issues such as misalignment, they can exhibit accuracy degradation when comparing images of differing resolutions. This is common in surveillance environments where a gallery of high resolution mugshots is compared to low resolution CCTV probe images, or where the size of a given image is not a reliable indicator of the underlying resolution (eg. poor optics). To alleviate this degradation, we propose a compensation framework which dynamically chooses the most appropriate face recognition system for a given pair of image resolutions. This framework applies a novel resolution detection method which does not rely on the size of the input images, but instead exploits the sensitivity of local features to resolution using a probabilistic multi-region histogram approach. Experiments on a resolution-modified version of the "Labeled Faces in the Wild" dataset show that the proposed resolution detector frontend obtains a 99% average accuracy in selecting the most appropriate face recognition system, resulting in higher overall face discrimination accuracy (across several resolutions) compared to the individual baseline face recognition systems.
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