Cross-Domain Palmprint Recognition Based on Transfer Convolutional Autoencoder

Abstract

Recently, excellent palmprint recognition algorithms have emerged and achieved satisfactory performance. However, cross-domain palmprint recognition is rarely considered. In this paper, we proposed transfer autoencoder for crossdomain palmprint recognition. Convolutional autoencoders were firstly used to extract low-dimensional features. A discriminator was then introduced to reduce the gap of two domains. The autoencoders and discriminator were alternately trained, and finally the features with the same distribution were extracted. The databases collected from different environments are defined as source and target domains, respectively. Based on the labels in source domain, unsupervised identification of target domain can be achieved. The experiments were performed on 24 cross-domain pairs composed by multispectral database and our self-built uncontrolled databases. The results show that transfer autoencoder can greatly improve cross-domain recognition accuracy, up to 23.26%. At the same time, the accuracy in a single domain can reach over 99% in controlled database.