Bimodal vein data mining via cross-selected-domain knowledge transfer

Recent success in large-scale image recognition challenge (i.e., ImageNet) fully demonstrates the capability of deep neural network (DNN) in learning complex and semantic representation, and this also motivates the generation of transfer learning model, which fine-tunes state-of-the-art DNN models with other small-scale databases for better performance. Driven by such an idea, a task-specific DNN model fine-tuned from VGG-face is constructed for both gender and identity recognition with hand vein information. Unlike the traditional transfer learning models, which fine-tune directly from source to target, we leverage the coarse-to-fine scheme to train the task-specific models in a step-aware way, such that the inherent correlation between the neighboring databases could serve as initialization base to relieve the problem of over-fitting, which is inevitable with the small-scaled hand vein database, and also speed up the convergence. Besides, the task-driven network training idea, which involves joint optimization of linear regression classifier and network parameters, is also adopted during training of each model to obtain more discriminative representation for specified tasks. Instead of adopting the trained linear regression classifier for gender and identity classification, the large margin distribution machine (LDM) is introduced to ensure the discriminative and generalization performance of the model simultaneously, and it should be noted that before feeding the gender feature vector into the LDM, a supervised feature selection step is incorporated to improve the classification performance by discarding the redundant feature and highlighting the important ones for gender classification. Rigorous experiments using the lab-made database are conducted to demonstrate the effectiveness and feasibility of the proposed model. What is more, additional experiment with a subset of the PolyU database illustrates its generalization ability and robustness.