US 11,816,922 B2
Fingerprint extraction apparatus and method
Byoung Ho Lee, Seoul (KR); Jae Bum Cho, Gwangmyeong-si (KR); Dong Heon Yoo, Daejeon (KR); Min Seok Chae, Seoul (KR); and Ju Hyun Lee, Seoul (KR)
Assigned to Seoul National University R&DB Foundation, Seoul (KR)
Appl. No. 16/975,244
Filed by SEOUL NATIONAL UNIVERSITY R&DB FOUNDATION, Seoul (KR)
PCT Filed Dec. 31, 2019, PCT No. PCT/KR2019/018806
§ 371(c)(1), (2) Date Aug. 24, 2020,
PCT Pub. No. WO2021/125422, PCT Pub. Date Jun. 24, 2021.
Claims priority of application No. 10-2019-0171036 (KR), filed on Dec. 19, 2019.
Prior Publication US 2023/0118211 A1, Apr. 20, 2023
Int. Cl. G06V 40/12 (2022.01); G06V 10/70 (2022.01); G06T 11/00 (2006.01)
CPC G06V 40/1359 (2022.01) [G06T 11/00 (2013.01); G06V 10/70 (2022.01)] 12 Claims
OG exemplary drawing
 
1. A fingerprint extraction apparatus comprising:
a fingerprint generation module configured to generate at least one first virtual fingerprint image comprising a fingerprint located vertically on a center of an image, and a plurality of second virtual fingerprint images different from the at least one first virtual fingerprint image, perform primary image processing on the at least one first virtual fingerprint image, perform primary image processing and secondary image processing on each of the plurality of second virtual fingerprint images, and combine the at least one first virtual fingerprint image on which the primary image processing is performed and the plurality of second virtual fingerprint images on which the at least one of primary image processing and secondary image processing is performed to generate a plurality of virtual overlapped fingerprint images;
a machine learning module configured to generate a learning model for extracting a target fingerprint by performing machine learning using the plurality of virtual overlapped fingerprint images as input data and the at least one first virtual fingerprint image as output data; and
a fingerprint extraction module configured to extract a fingerprint located vertically on a center of a real image by inputting the real image to the learning model,
wherein the primary image processing comprises image processing on a curve forming a fingerprint, and the secondary image processing comprises image processing on a location of the fingerprint in the image, and
wherein the primary image processing further comprises at least one of adding a curve of the fingerprint, removing a curve of the fingerprint, and changing a thickness of the fingerprint, and the secondary image processing comprises at least one of rotating the fingerprint and inverting the fingerprint.