In this paper, we propose a dorsal hand vein recognition system using Convolutional Neural Network
(CNN). This system is automatically learned how to extract features from original image without preprocessing. The
proposed system has two approaches: the first one is using the pre-trained CNN models (AlexNet, VGG16 and
VGG19) for extracting features from 'fc6','fc7' and 'fc8' layers then using Error-Correcting Output Codes (ECOC)
with Support Vector machine (SVM) and K-Nearest Neighbor (K-NN) algorithms for the classification. The second
approach is using transfer learning with CNN (AlexNet, VGG16 and VGG19) models for both features’ extraction
and classification. The experiments applied on two different datasets: Dr. Badawi hand veins dataset that contains
500 image and BOSPPHORUS dorsal vein dataset that contains 1575 images. In the first approach experiment, the
recognition accuracy of all models gives best result when features are extracted from 'fc6'. Also, the accuracy rate of
the models that use ECOC with SVM for classification is higher than the models that use ECOC with KNN and the
VGG19 model achieves better results in models that use ECOC with SVM. In the second approach experiment, the
recognition accuracy for all models give best result when epoch number is 50 where Dr. Badawi dataset in VGG16
and AlexNet reaches to 100% recognition rate and BOSPPHORUS dataset reaches to 99.25 % recognition rate in
VGG19. Finally, the discussion concluded that using transfer learning is giving more accuracy rate than using the
pre-trained CNN models for extracting features