Multi-Scale Deep Representation Aggregation for Vein Recognition

The recent success of Deep Convolutional Neural Network (DCNN) for various computer vision tasks such as image recognition has already demonstrated its robust feature representation ability. However, the limitation of training database on small scale vein recognition tasks restricts its performance because the recognition result of DCNN depends heavily on the number of trainsets. This motivates the design of a Multi-Scale Deep Representation Aggregation (MSDRA) model based on a pre-trained DCNN for vein recognition. First, the multi-scale feature maps are extracted by a pre-trained DCNN model. Second, a local mean threshold approach is designed to preliminarily remove the noisy information of multi-scale feature maps and generate the selected feature maps. Third, we propose an Unsupervised Vein Information Mining (UVIM) method to localize vein information of selected feature maps for generating a binary vein information mask, and then the vein information mask is utilized to keep useful deep representation and discard the background information. Finally, the discriminative multi-scale deep representations, which are generated by using the vein information mask to aggregate multi-scale feature maps, are concatenated into the final compact feature vectors, and then a Support Vector Machine (SVM) is introduced for final recognition. Our proposed model outperforms the state-of-the-art methods on two benchmark vein databases. Moreover, an additional experiment using the subset of PolyU Palmprint database illustrates the system's generalization ability and robustness.