Abstract
Nowadays, palmprint recognition has been well developed since plenty of promising algorithms have emerged. Palmprints have also been applied under various authentication scenarios. However, these approaches are designed and tested only when the registration images and probe images are taken under the same illumination condition; thus, a cross-spectral performance degradation is speculated. Therefore, we test the cross-spectral performance of extended binary orientation co-occurrence vector (E-BOCV), which is unsatisfactory, illustrating the necessity of a specific algorithm. Trying to achieve the cross-spectral palmprint recognition with image-to-image translation, we have made efforts in the following two aspects. First, we introduce a scheme to evaluate the images of different spectra, which is a reliable basis for translation direction determination. Second, in this paper, we propose a palmprint translation convolutional neural network (PT-net) and the performance of translation from NIR to blue is tested on the PolyU multispectral dataset, which achieves a 91% decrease in Top-1 error using E-BOCV as the recognition framework.
Highlights
Palmprint recognition has been a hot topic for a long time
We focus on removing the feature gap of multispectral palmprint images and defining the problem as palmprint image translation
Derived from Coupled generative adversarial network (GAN), an unsupervised image translation framework was proposed by Liu [17], where the shared latent space assumption was made
Summary
Palmprints are much larger in area with more texture features which makes them stable in recognition. It has been widely applied in many safety-concerned scenarios, such as identification in hospitals and banks. Shao et al [5] introduced an autoencoder to extract shared features in different domains for verification. These methods have their limitations as they attempt to solve two problems simultaneously. Both the feature gap between the spectra and the performance of the recognition algorithm have massive impact on the overall capability. Training details are discussed, including the data alignment method and loss function hyperparameter setting
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