by Fieke Hillerström
Deep learning for biometrics has increasingly gained attention over the last years. Due to the expansion of computational power and the increasing sizes of the available datasets, the performance has surpassed that of humans on certain verification tasks. However, large datasets are not available for every application. Therefore we introduce Deep Verification Learning, to reduce network complexity and train with more modest hardware on smaller datasets. Deep Verification Learning takes two images to be verified at the input of a deep learning network, and trains directly towards a verification score. This topology enables the network to learn differences and similarities in the first layer, and to involve verification signals during training. Directly training towards a verification score reduces the number of trainable parameters significantly. We applied Deep Verification Learning on the face verification task, also it could be extended to other biometric modalities. We compared our face verification learning topology with a network trained for multi-class classification on the FRGC dataset, which contains only 568 subjects. Deep Verification Learning performs substantially better.