Paper: Learning a Compact Vein Discrimination Model With GANerated Samples

June 23rd, 2020

Despite the great success achieved by convolutional neural networks (CNNs) in various image understanding tasks, it is still difficult for CNNs to be applied to vein recognition tasks due to the problems of insufficient training datasets, intra-class variations, and inter-class similarities.

Besides, due to the essential requirement on the storage of millions of parameters for CNN, it is challenging to use a CNN for designing a vein-based embedded person identification system.

In this paper, these two problems are addressed by learning a discriminative and compact vein recognition model. For the first problem, a hierarchical generative adversarial network (HGAN) consisting of a constrained CNN and a CycleGAN is proposed for data augmentation.

Two similarity losses are defined for estimating the self-similarity and inter-class dissimilarity, and a CycleGAN model is properly trained with these two losses for better task-specific training sample generation.

After obtaining a baseline vein recognition model fine-tuned on the augmented datasets, the existence of parameter redundancy in the over-parameterized network motivates the proposal of model compression by way of filter pruning and low rank approximation, thus making the compressed model more suitable for deployment on embedded systems.

Through the vein recognition experiments with two different datasets and an additional palmprint recognition experiment, the proposed algorithms are shown to yield a highly compact model while keeping the accuracy acceptable for application.

Fig. 2. Overview of our approach for learning the discriminative and compact vein recognition model. Step (1) represents fine-tuning a basic model with the original dataset, Step (2) represents using the output from the basic model to derive the image domain labels, Step (3) represents HGAN based dataset augmentation, Step (4) represents basic model re-training with the augmented dataset, Step (5) represents the redundancy induced model compression.

Wang, Guoqing; Sun, Changming; Sowmya, Arcot. Learning a compact vein discrimination model with GANerated samples. IEEE Transactions on Information Forensics and Security. 2020; 15:635-650.

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