Palm Vein Recognition Algorithm using Multilobe Differential Filters

Proceedings of the 31th International Conference on Computer Graphics and Vision. Volume 2

2021

Self Cite

In this article the new neural network algorithm for palm vein identification using the triplet loss function is proposed. The neural network model is based on the VGG16 architecture. The similarity learning problem instead of the classification problem is considered. The number of image classes is assumed to be unknown so at the output of the neural network the feature vector is obtained, and then for the pair of palm vein images the distance between them is calculated. Minimization of triplet loss function while training leads to the decrease in distances between the images of the same class, while the distances between the images of different classes increase. The neural network was trained using preprocessed and segmented images from CASIA multi-spectral palmprint image database. The use of segmentation information for palm vein recognition improves the recognition results. Experimental results demonstrate the effectiveness of the proposed method. The value of EER=0.0084 is obtained.

Palm Vein Identification Based on Vein Segmentation and Triplet Loss Function

Trofimov

Proceedings of the 31th International Conference on Computer Graphics and Vision. Volume 2

2021

Self Cite

Hybrid Iris Segmentation Method Based on CNN and Principal Curvatures

Tikhonova

Proceedings of the 30th International Conference on Computer Graphics and Machine Vision (GraphiCon 2020). Part 2

2020

Self Cite

In this article the new hybrid iris image segmentation method based on convolutional neural networks and mathematical methods is proposed. Iris boundaries are found using modified Daugman’s method. Two UNet-based convolutional neural networks are used for iris mask detection. The first one is used to predict the preliminary iris mask including the areas of the pupil, eyelids and some eyelashes. The second neural network is applied to the enlarged image to specify thin ends of eyelashes. Then the principal curvatures method is used to combine the predicted by neural networks masks and to detect eyelashes correctly. The pro- posed segmentation algorithm is tested using images from CASIA IrisV4 Interval database. The results of the proposed method are evaluated by the Intersection over Union, Recall and Precision metrics. The average metrics values are 0.922, 0.957 and 0.962, respectively. The proposed hy- brid iris image segmentation approach demonstrates an improvement in comparison with the methods that use only neural networks.

Unsupervised Palm Vein Image Segmentation

Safronova

Proceedings of the 30th International Conference on Computer Graphics and Machine Vision (GraphiCon 2020). Part 2

2020

Self Cite

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.