Size Optimized Generative Adversarial Networks for Single Image Super-Resolution of Fingerprint Images for Big Data Applications - Lisha P. P. (International Journal of Intelligent Systems and Applications in Engineering, 2024)
Big data applications such as the Aadhar project necessitate the storage of large volumes of biometric data, which requires about 20,218 TB of storage space. Big data projects, which present enormous storage and processing challenges, demand large storage servers and high-end computers. This paper suggests a novel method to enhance the resolution of compressed fingerprint images using a super-resolution (SR) model that reconstructs a high-resolution (HR) image from a low-resolution (LR) image can significantly reduce the requirement for large amounts of data and costly hardware in such cases. This proposed size optimized GAN (Generative Adversarial Network) based SR model of size 2.9 MB, that enlarges a low-resolution image to a scale factor of eight times. The model was trained on the fingerprint data set FVC 2004 (Fingerprint Verification Competition) and then tested on the FVC 2004 data set. The various human visual system (HVS) parameters, such as peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and mean squared error (MSE), were measured, and their values were found to be 35.97, 0.958, and 19.5, respectively. Perceptual loss was measured in terms of generator loss as 0.981 and discriminator loss as 0.550. The accuracy of matching between the ground-truth image and the regenerated image was measured through the SIFT (scale-invariant feature transform) method and obtained an identification accuracy of 98.7%. This approach has the potential to increase the performance of fingerprint recognition systems for latent fingerprint images.