Generative Adversarial Network (GAN) Based Data Augmentation for Palmprint Recognition

Palmprint recognition is a very important field of biometrics, and has been intensively researched on both feature extraction and classification methods. Recently, deep learning techniques such as convolutional neural networks have demonstrated clear advantages over traditional learning algorithms for various image classification tasks such as object recognition and detection. However, a large amount of data is needed to train deep networks, which limits its application to some tasks such as palmprint recognition where it lacks of sufficient training samples for each class (i.e., each individual). In this paper, we propose a Generative Adversarial Net (GAN) based solution to augment training data for improved performance of palmprint recognition. An improved Deep Convolutional Generative Adversarial Net (DCGAN) is first devised to generate high quality plamprint images by replacing convolutional transpose layer with linear upsampling and introducing Structure Similarity (SSIM) index into loss function. As a result, the generated images have discriminative features, increased smoothness and consistency, and less variance compared to those generated by the baseline DCGAN. Then, a mixing training strategy via a combination of GAN-based and classical data augmentation techniques is adopted to further improve recognition performance. The experimental results on two publicly available datasets demonstrate the effectiveness of our proposed GAN based data augmentation method in palmprint recognition. Our method is able to achieve 1.52% and 0.37% Equal Error Rates (EER) on IIT Delhi and CASIA palmprint datasets, respectively, which outperforms other existing methods.