Palmprint recognition via discriminative index learning
Jan Svoboda, Jonathan Masci, Michael M. Bronstein · 2016
In the past years, deep convolutional neural networks (CNNs) have become extremely popular in the computer vision and pattern recognition community. The computational power of modern processors, efficient stochastic optimization algorithms, and large amounts of training data allowed training complex tasks-specific features directly from the data in an end-to-end fashion, as opposed to the traditional way of using hand-crafted feature descriptors. CNNs are currently state-of-the-art methods in many computer vision problems, and have been successfully used in biometric applications such as face, fingerpring, and voice recognition. In palmprint recognition applications, CNNs have not yet been explored, and the majority of methods still rely on hand-crafted representations which do not scale well to large datasets and that usually require a complex manual parameter tuning. In this work, we show that CNNs can be successfully used for palmprint recognition. The training of our network uses a novel loss function related to the d-prime index, which allows to achieve a better genuine/impostor score distribution separation than previous approaches with only little training data required. Our approach does not require cumbersome parameter tuning and achieves state-of-the-art results on the standard IIT Delhi and CASIA palmprint datasets.