TY - JOUR
T1 - Efficient hardware implementation strategy for local normalization of fingerprint images
AU - Khan, Tariq M.
AU - Bailey, Donald G.
AU - Khan, Mohammad A. U.
AU - Kong, Yinan
PY - 2019/8
Y1 - 2019/8
N2 - Global techniques do not produce satisfying and definitive results for fingerprint image normalization due to the non-stationary nature of the image contents. Local normalization techniques are employed, which are a better alternative to deal with local image statistics. Conventional local normalization techniques involve pixelwise division by the local variance and thus have the potential to amplify unwanted noise structures, especially in low-activity background regions. To counter the background noise amplification, the research work presented here introduces a correction factor that, once multiplied with the output of the conventional normalization algorithm, will enhance only the feature region of the image while avoiding the background area entirely. In essence, its task is to provide the job of foreground segmentation. A modified local normalization has been proposed along with its efficient hardware structure. On the way to achieve real-time hardware implementation, certain important computationally efficient approximations are deployed. Test results show an improved speed for the hardware architecture while sustaining reasonable enhancement benchmarks.
AB - Global techniques do not produce satisfying and definitive results for fingerprint image normalization due to the non-stationary nature of the image contents. Local normalization techniques are employed, which are a better alternative to deal with local image statistics. Conventional local normalization techniques involve pixelwise division by the local variance and thus have the potential to amplify unwanted noise structures, especially in low-activity background regions. To counter the background noise amplification, the research work presented here introduces a correction factor that, once multiplied with the output of the conventional normalization algorithm, will enhance only the feature region of the image while avoiding the background area entirely. In essence, its task is to provide the job of foreground segmentation. A modified local normalization has been proposed along with its efficient hardware structure. On the way to achieve real-time hardware implementation, certain important computationally efficient approximations are deployed. Test results show an improved speed for the hardware architecture while sustaining reasonable enhancement benchmarks.
KW - Embedded image processing
KW - FPGA
KW - Biometrics
UR - http://www.scopus.com/inward/record.url?scp=84979211622&partnerID=8YFLogxK
U2 - 10.1007/s11554-016-0625-8
DO - 10.1007/s11554-016-0625-8
M3 - Article
AN - SCOPUS:84979211622
VL - 16
SP - 1263
EP - 1275
JO - Journal of Real-Time Image Processing
JF - Journal of Real-Time Image Processing
SN - 1861-8200
IS - 4
ER -