Multi-loss Function in Robust Convolutional Autoencoder for Reconstruction Low-quality Fingerprint Image

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 (P...

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Bibliographic Details
Published in:Proceedings ... Asia-Pacific Signal and Information Processing Association Annual Summit and Conference APSIPA ASC ... (Online) pp. 428 - 431
Main Authors: Raswa, Farchan Hakim, Halberd, Franki, Harjoko, Agus, Wahyono, Lee, Chung-Ting, Li, Yung-Hui, Wang, Jia Ching
Format: Conference Proceeding
Language:English
Published: Asia-Pacific of Signal and Information Processing Association (APSIPA) 07.11.2022
Subjects:
convolution autoencoder
fingerprint
Fingerprint recognition
Image matching
Image quality
loss function
Measurement uncertainty
Propagation losses
PSNR
reconstruction
Weight measurement
ISSN:2640-0103
Online Access:Get full text
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Summary: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.
ISSN:2640-0103
DOI:10.23919/APSIPAASC55919.2022.9980345