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首页> 外文期刊>IEEE transactions on information forensics and security >Matching NIR Face to VIS Face Using Transduction
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Matching NIR Face to VIS Face Using Transduction

机译：使用转导将NIR人脸与VIS人脸匹配

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Visual versus near infrared (VIS-NIR) face image matching uses an NIR face image as the probe and conventional VIS face images as enrollment. It takes advantage of the NIR face technology in tackling illumination changes and low-light condition and can cater for more applications where the enrollment is done using VIS face images such as ID card photos. Existing VIS-NIR techniques assume that during classifier learning, the VIS images of each target people have their NIR counterparts. However, since corresponding VIS-NIR image pairs of the same people are not always available, which is often the case, so those methods cannot be applied. To address this problem, we propose a transductive method named transductive heterogeneous face matching (THFM) to adapt the VIS-NIR matching learned from training with available image pairs to all people in the target set. In addition, we propose a simple feature representation for effective VIS-NIR matching, which can be computed in three steps, namely Log-DoG filtering, local encoding, and uniform feature normalization, to reduce heterogeneities between VIS and NIR images. The transduction approach can reduce the domain difference due to heterogeneous data and learn the discriminative model for target people simultaneously. To the best of our knowledge, it is the first attempt to formulate the VIS-NIR matching using transduction to address the generalization problem for matching. Experimental results validate the effectiveness of our proposed method on the heterogeneous face biometric databases.
机译：视觉与近红外（VIS-NIR）脸部图像匹配使用NIR脸部图像作为探针，并使用常规VIS脸部图像作为注册。它利用NIR人脸技术来应对照明变化和弱光条件，并且可以满足使用VIS人脸图像（例如身份证照片）进行注册的更多应用。现有的VIS-NIR技术假设在分类器学习期间，每个目标人群的VIS图像都有其NIR对应物。但是，由于并非总是可以得到同一个人的相应VIS-NIR图像对（通常是这种情况），因此无法应用这些方法。为了解决这个问题，我们提出了一种名为“转导异构脸部匹配”（THFM）的转导方法，以将通过使用可用图像对训练获得的VIS-NIR匹配适应目标集中的所有人。此外，我们提出了一种有效的VIS-NIR匹配的简单特征表示，可以通过三步计算，即Log-DoG滤波，局部编码和统一特征归一化，以减少VIS和NIR图像之间的异质性。转导方法可以减少由于异构数据导致的域差异，并同时学习针对目标人群的判别模型。据我们所知，这是首次尝试使用转导来表达VIS-NIR匹配，以解决匹配的泛化问题。实验结果验证了我们提出的方法在异构面部生物特征数据库上的有效性。

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