Generative Adversarial Networks (GANs) can be used for data augmentation in order to improve the outcome and performance of machine learning models for automatic information retrieval. We looked into the challenge faced with limited blurry and distorted digit images from expiry dates datasets, which is required to improve digit recognition tasks on medicine, consumables, cosmetic products and tube-type ointments. For our dataset, Wasserstein GAN with a gradient norm penalty (WGAN-GP) was effective for data augmentation among the state-of-the-art GANs by visible inspection and Fréchet Inception Distance (FID) value comparison.

Various methods of personal identification and authentication using biometric data have been developed. However, there are downsides to these techniques due to the risk of data leakage during the authentication process and the data acquisition environment required for recognition. We propose a scheme to identify the residents in the daily living space through pairs of real and thermal images taken from Internet of Things (IoT) cameras monitoring the space.