Adversarial Learning-Based Data Augmentation for Palm-Vein Identification

Palm-vein identification is a highly secure pattern biometrics that has become an active research area in recent years. Despite the recent progress in deep neural networks (DNNs) for vein identification, existing solutions for feature representation continue to lack robustness due to the limited training samples. To address this limitation, data augmentation approaches, including Generative Adversarial Networks (GANs), have been investigated, but these schemes suffer from the following issues. First, it is practically unfeasible to use all the generated samples for classifier training due to the limited storage space and computation resources. Further, some of these generated samples may be non-representative or ineffective, seriously compromising models’ generalization capabilities. Second, the augmented dataset is fed to the target classifier repeatedly, resulting in overfitting after substantial training epochs. To tackle the above problems, we propose Advein
$\mathbb {AU}$
, an Adversarial vein AUtomatic AUgmentation approach that generates challenging samples to train a more robust vein classifier for palm-vein identification by alternatively optimizing the vein classifier and a set of latent variables. First, we consider a conditional deep convolution generative adversarial net (cDCGAN) to learn the distribution of real data and the generated data, and then a latent variable from the latent variable space is mapped to the sample space. Second, we combine the trained generator with the vein classifier to constitute Advein
$\mathbb {AU}$
, where the input sets of the generator and the classifier are alternatively updated by adversarial training. Specifically, a latent variable set is learned to increase the training loss of a target network through generating adversarial samples, while the classifier learns more robust features from harder examples to improve the generalization. To avoid collapsing inherent meanings of images, an exponential moving average (EMA) teacher and
$cosine$
similarity are employed for regularization to reduce the search space. Unlike previous works where GANs synthesize new realistic images, our model aims to search for a latent variable set, based on which the generator can produce challenging samples along with the training process to improve the classifier’s performance. Finally, we conduct extensive experiments on three public palm-vein datasets to evaluate the performance of Advein
$\mathbb {AU}$
, and the experimental results demonstrate that the proposed Advein
$\mathbb {AU}$
is capable of generating harder samples to improve the performance of the vein classifier.