TY - GEN
T1 - STUA-Net
T2 - 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023
AU - Hakim, Farchan Raswa
AU - Wicaksana, Prabowo Yoga
AU - Putri, Wenny Ramadha
AU - Harjoko, Agus
AU - Wang, Jia Ching
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Fingerprints play a vital role in person authentication and verification. To achieve accurate recognition, fingerprint images should contain 25 to 80 minutiae points, which define the unique characteristics of a fingerprint. However, due to various factors such as changes in the environment, the fingerprint structure can become corrupted, resulting in low-quality fingerprints. This corruption leads to a limited number of extractable minutiae points, making it challenging to establish the uniqueness of an individual. In this paper, we propose STUA-Net, a novel approach that incorporates Swin Transformer into the encoding and decoding layers to effectively map corrupted regions. Additionally, we introduce Soft Collective Attention to suppress the activation of relevant features. Our proposed method serves as a foundation for future research to improve recognition accuracy, particularly in scenarios involving low-quality fingerprints. It addresses an important problem in the field and contributes to the advancement of fingerprint recognition technology.
AB - Fingerprints play a vital role in person authentication and verification. To achieve accurate recognition, fingerprint images should contain 25 to 80 minutiae points, which define the unique characteristics of a fingerprint. However, due to various factors such as changes in the environment, the fingerprint structure can become corrupted, resulting in low-quality fingerprints. This corruption leads to a limited number of extractable minutiae points, making it challenging to establish the uniqueness of an individual. In this paper, we propose STUA-Net, a novel approach that incorporates Swin Transformer into the encoding and decoding layers to effectively map corrupted regions. Additionally, we introduce Soft Collective Attention to suppress the activation of relevant features. Our proposed method serves as a foundation for future research to improve recognition accuracy, particularly in scenarios involving low-quality fingerprints. It addresses an important problem in the field and contributes to the advancement of fingerprint recognition technology.
KW - Corrupted Region
KW - Fingerprint Reconstruction
KW - Soft Collective Attention
KW - Swin Transformer
UR - http://www.scopus.com/inward/record.url?scp=85180012928&partnerID=8YFLogxK
U2 - 10.1109/APSIPAASC58517.2023.10317455
DO - 10.1109/APSIPAASC58517.2023.10317455
M3 - 會議論文篇章
AN - SCOPUS:85180012928
T3 - 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023
SP - 2209
EP - 2212
BT - 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 31 October 2023 through 3 November 2023
ER -