Improved Siamese Palmprint Authentication Using Pre-Trained VGG16-Palmprint and Element-Wise Absolute Difference

Abstract

Palmprint identification has been conducted over the last two decades in many biometric systems. High-dimensional data with many uncorrelated and duplicated features remains difficult due to several computational complexity issues. This paper presents an interactive authentication approach based on deep learning and feature selection that supports Palmprint authentication. The proposed model has two stages of learning; the first stage is to transfer pre-trained VGG-16 of ImageNet to specific features based on the extraction model. The second stage involves the VGG-16 Palmprint feature extraction in the Siamese network to learn Palmprint similarity. The proposed model achieves robust and reliable end-to-end Palmprint authentication by extracting the convolutional features using VGG-16 Palmprint and the similarity of two input Palmprint using the Siamese network. The second stage uses the CASIA dataset to train and test the Siamese network. The suggested model outperforms comparable studies based on the deep learning approach achieving accuracy and EER of 91.8% and 0.082%, respectively, on the CASIA left-hand images and accuracy and EER of 91.7% and 0.084, respectively, on the CASIA right-hand images.

Attention pyramid pooling network for artificial diagnosis on pulmonary nodules

“…The proposed methodology is shown in Fig 1 . 2.1.1 Encoder. The vgg16 [34,35] architecture is a 16-layer deep neural network that consists of several convolutional layers, max pooling layers, and fully connected layers. Here is a detail description of each block and its structure, as shown in Table 1.…”