Picture-specific cohort score normalization for face pair matching
Picture-specific cohort score normalization for face pair matching
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DOI：
10.1109/btas.2013.6712738
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发表时间：
2013-09
期刊：
影响因子：
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通讯作者：
Yunlian Sun;M. Tistarelli;N. Poh
中科院分区：
文献类型：
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作者：
Yunlian Sun;M. Tistarelli;N. Poh
人脸配对是判断两张人脸图像是否属于同一个人的任务。由于面部图像中存在各种各样的变异源，特别是在不受约束的环境下，这是一个非常活跃和具有挑战性的话题。我们研究了在生物特征验证中广泛使用的队列归一化，作为提高挑战性环境下人脸识别对人脸配对问题的鲁棒性的手段。具体而言，给定一对图像和一个额外的固定队列集（队列样本的身份从未出现在测试阶段），计算两个特定图像的队列得分列表，并通过多项式回归建模其相应的得分曲线。提取的回归系数随后使用分类器进行分类。我们通过提供对队列行为的更好理解来推进队列规范化的最先进技术。特别是，我们发现队列集的选择对泛化性能的影响很小。此外，队列集的规模越大，系统性能越稳定。在野外标记面孔（LFW）基准上进行的实验表明，我们的系统达到了与最先进的方法相当的性能。
Face pair matching is the task of deciding whether or not two face images belong to the same person. This has been a very active and challenging topic recently due to the presence of various sources of variation in facial images, especially under unconstrained environment. We investigate cohort normalization that has been widely used in biomet-ric verification as means to improve the robustness of face recognition under challenging environments to the face pair matching problem. Specifically, given a pair of images and an additional fixed cohort set (identities of cohort samples never appear in the test stage), two picture-specific cohort score lists are computed and the correspondent score profiles of which are modeled by polynomial regression. The extracted regression coefficients are subsequently classified using a classifier. We advance the state-of-the-art in cohort normalization by providing a better understanding of the cohort behavior. In particular, we found that the choice of the cohort set had little impact on the generalization performance. Furthermore, the larger the size of the cohort set, the more stable the system performance becomes. Experiments performed on the Labeled Faces in the Wild (LFW) benchmark show that our system achieves performance that is comparable to state-of-the-art methods.