TY - GEN
T1 - Non-intrusive fingerprints extraction from hyperspectral imagery
AU - Yan, Longbin
AU - Chen, Jie
N1 - Publisher Copyright:
© EURASIP 2018.
PY - 2018/11/29
Y1 - 2018/11/29
N2 - Fingerprint extraction plays an important role in criminal investigation and information security. Conventionally, latent fingerprints are not readily visible and imaging often requires to use intrusive manners. Hyperspectral imaging techniques provide a possibility to extract fingerprints in a non-intrusive manner, however it requires well-designed image analysis algorithms. In this paper, we consider the problem of fingerprint extraction from hyperspectral images and propose a processing scheme. The proposed scheme extracts image textures by local total variation (LTV) and uses Histogram of Oriented Gradient (HOG) information to fuse these channels. Experiment results with a real image show the ability of the proposed method for extracting fingerprints from complex backgrounds.
AB - Fingerprint extraction plays an important role in criminal investigation and information security. Conventionally, latent fingerprints are not readily visible and imaging often requires to use intrusive manners. Hyperspectral imaging techniques provide a possibility to extract fingerprints in a non-intrusive manner, however it requires well-designed image analysis algorithms. In this paper, we consider the problem of fingerprint extraction from hyperspectral images and propose a processing scheme. The proposed scheme extracts image textures by local total variation (LTV) and uses Histogram of Oriented Gradient (HOG) information to fuse these channels. Experiment results with a real image show the ability of the proposed method for extracting fingerprints from complex backgrounds.
KW - Fingerprint extraction
KW - Histogram of oriented gradient
KW - Hyperspectral images
KW - Local total variation
KW - Texture
UR - http://www.scopus.com/inward/record.url?scp=85059819551&partnerID=8YFLogxK
U2 - 10.23919/EUSIPCO.2018.8553281
DO - 10.23919/EUSIPCO.2018.8553281
M3 - 会议稿件
AN - SCOPUS:85059819551
T3 - European Signal Processing Conference
SP - 1432
EP - 1436
BT - 2018 26th European Signal Processing Conference, EUSIPCO 2018
PB - European Signal Processing Conference, EUSIPCO
T2 - 26th European Signal Processing Conference, EUSIPCO 2018
Y2 - 3 September 2018 through 7 September 2018
ER -
