A Criterion for Analysis of Different Sensor Combinations with an Application to Face Biometrics
A Criterion for Analysis of Different Sensor Combinations with an Application to Face Biometrics
复制标题
不同传感器组合分析的标准及其在面部生物识别中的应用
DOI：
10.1007/s12559-010-9060-5
复制
发表时间：
2010
影响因子：
5.4
通讯作者：
E. Monte‐Moreno
中科院分区：
文献类型：
--
作者：
V. Espinosa;M. Faúndez;J. Mekyska;E. Monte‐Moreno
在本文中，我们提出了一个标准，成对组合的信息从不同的传感器，以决定如何一个给定的一对传感器是有用的不同的应用。这一标准与最大限度地保存信息的原则有关。我们目前的实验结果的情况下，在不同的光谱带，这使得在不同的传感器组合的有用性，以及交叉传感器识别（在不同的光谱带中获取的图像匹配）的可能性提前评估的人脸图像。我们提出的标准是一个泛化的Fisher得分的情况下的互信息，这是衡量的类间信息的比例，以类内。我们提出的分数测量一对传感器的行为，无论是当它们被组合使用时，还是当它们被用来区分类别时。基于信息论的测量，我们得出结论，最好的光谱波段组合总是包含热图像，而交叉传感器识别的最佳组合是维斯和NIR。
In this paper, we propose a criterion for pairwise combination of information from different sensors in order to decide how a given pair of sensors is useful for different applications. This criterion is related to the principle of maximum information preservation. We present experimental results for the case of face images at different spectral bands, which allow for the in advance evaluation of the usefulness of different sensor combinations as well as the possibility for crossed-sensor recognition (matching of images acquired in different spectral bands). The criterion that we propose is a generalization of the Fisher score for the case of mutual information, which is measured as the ratio of the interclass information to the intraclass. The score we propose measures the behavior of a pair of sensors either when they are used in combination or when they are used to discriminate between classes. Based on Information Theory measurements, we conclude that the best spectral band combination always contains the thermal image, while the best combination for crossed-sensor recognition is VIS and NIR.