Abstract
In this article the new hybrid algorithm for palm vein image segmentation using convolutional neural network and principal curvatures is proposed. After palm vein image preprocessing vein structure is detected using unsupervised learning approach based on W-Net architecture, that ties together into a single autoencoder two fully convolutional neural network architectures, each simi-lar to the U-Net. Then segmentation results are improved using principal cur-vatures technique. Some vein points with highest maximum principal curva-ture values are selected, and the other vein points are found by moving from starting points along the direction of minimum principal curvature. To obtain the final vein image segmentation the result of intersection of the principal curvatures-based and neural network-based segmentations is taken. The evaluation of the proposed unsupervised image segmentation method based on palm vein recognition results using multilobe differential filters is given. Test results using CASIA multi-spectral palmprint image database show the effectiveness of the proposed segmentation approach.
Highlights
Nowadays information security plays crucial role in human life and, as it turned out, accustomed keys and passwords are not reliable enough
In this paper we propose a hybrid segmentation method consisting of two approaches: based on convolutional neural networks (CNN) and principal curvatures (Fig.1)
Given the intra- and interclass vein matching results, the recognition performance is measured by the following indicators: the distribution of genuine and impostor scores, False Acceptance Rate (FAR), False Reject Rate (FRR) and Equal Error Rate (EER) − the cross-over error rate when FAR is equal to FRR
Summary
Nowadays information security plays crucial role in human life and, as it turned out, accustomed keys and passwords are not reliable enough. The last algorithm step, image matching, is based on feature vector type. Deep learning methods can be applied to any step of palm vein recognition algorithm [10, 11, 12, 13]. In this paper we propose a hybrid approach based on unsupervised machine learning and mathematical methods to obtain good vein segmentation. In this paper we propose a hybrid segmentation method consisting of two approaches: based on CNN and principal curvatures (Fig.).
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