Multi-loss Function in Robust Convolutional Autoencoder for Reconstruction Low-quality Fingerprint Image - Raswa, Farchan Hakim; Halberd, Franki; Harjoko, Agus; Wahyono; Lee, Chung-Ting; Li, Yung-Hui; Wang, Jia Ching (Proceedings ... Asia-Pacific Signal and Information Processing Association Annual Summit and Conference APSIPA ASC ... (Online), 2022)
Our research is fingerprint reconstruction based on a convolutional autoencoder. We combine the perceptual measurement as a multi-loss function to give satisfactory weight correction, such as the structural similarity index measure (SSIM), Mean Absolute Error (MAE), and peak signal-to-noise ratio (PSNR). We observed and investigated the result using multi-loss functions and other loss functions. Eventually, our experiment obtained the highest image quality metric scores from the experimental result summarized as a loss function (SSIM + PSNR) with optimizer Root Mean Squared Propagation (RMSprop). We evaluated the image reconstruction using a dataset from FVC2004. Eventually, our proposed method gets impressive results, increasing the image's average quality by PSNR of 20.58%, SSIM of 4.07%, and MSE of 38.92%, respectively.