TY - GEN
T1 - Multi-loss Function in Robust Convolutional Autoencoder for Reconstruction Low-quality Fingerprint Image
AU - Raswa, Farchan Hakim
AU - Halberd, Franki
AU - Harjoko, Agus
AU - Wahyono,
AU - Lee, Chung Ting
AU - Li, Yung-Hui
AU - Wang, Jia Ching
N1 - Publisher Copyright:
© 2022 Asia-Pacific of Signal and Information Processing Association (APSIPA).
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - convolution autoencoder
KW - fingerprint
KW - loss function
KW - reconstruction
UR - http://www.scopus.com/inward/record.url?scp=85146262405&partnerID=8YFLogxK
U2 - 10.23919/APSIPAASC55919.2022.9980345
DO - 10.23919/APSIPAASC55919.2022.9980345
M3 - 會議論文篇章
AN - SCOPUS:85146262405
T3 - Proceedings of 2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2022
SP - 428
EP - 431
BT - Proceedings of 2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2022
Y2 - 7 November 2022 through 10 November 2022
ER -