Abstract
Maximum Curvature Method (MCM) is one of the promising methods for finger vein verification. MCM scans the curvature of the vein image profiles within a finger for feature extraction. However, the quality of the image can be poor due to variations in illumination and sensor conditions. Furthermore, traditional MCM matching of the vein pattern requires extensive processing time. To address these limitations, we propose an integrated Enhanced Maximum Curvature (EMC) method with Histogram of Oriented Gradient (HOG) descriptor for finger vein verification. Unlike MCM, EMC incorporates an enhancement mechanism to extract small vein delineation that is hardly visible in the extracted vein patterns. Next, HOG is applied instead of image binarization to convert a two-dimensional vein image into a one-dimensional feature vector for efficient matching. The HOG descriptor is able to characterize the local spatial representation of a finger vein by capturing the gradient information effectively. The proposed method is evaluated based on two datasets namely the PKU Finger Vein Database (V4) and SDUMLA-HMT finger vein database. Experiments show promising verification results with equal error rates as low as 0.33 % for DB1 and 0.14 % for DB2 respectively, when EMC+HOG+SVM is applied.
| Original language | English |
|---|---|
| Pages (from-to) | 6859-6887 |
| Number of pages | 29 |
| Journal | Multimedia Tools and Applications |
| Volume | 76 |
| Issue number | 5 |
| DOIs | |
| Publication status | Published - 2017 Mar 1 |
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All Science Journal Classification (ASJC) codes
- Software
- Media Technology
- Hardware and Architecture
- Computer Networks and Communications
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Enhanced maximum curvature descriptors for finger vein verification. / Syarif, Munalih Ahmad; Ong, Thian Song; Teoh, Beng Jin; Tee, Connie.
In: Multimedia Tools and Applications, Vol. 76, No. 5, 01.03.2017, p. 6859-6887.Research output: Contribution to journal › Article
TY - JOUR
T1 - Enhanced maximum curvature descriptors for finger vein verification
AU - Syarif, Munalih Ahmad
AU - Ong, Thian Song
AU - Teoh, Beng Jin
AU - Tee, Connie
PY - 2017/3/1
Y1 - 2017/3/1
N2 - Maximum Curvature Method (MCM) is one of the promising methods for finger vein verification. MCM scans the curvature of the vein image profiles within a finger for feature extraction. However, the quality of the image can be poor due to variations in illumination and sensor conditions. Furthermore, traditional MCM matching of the vein pattern requires extensive processing time. To address these limitations, we propose an integrated Enhanced Maximum Curvature (EMC) method with Histogram of Oriented Gradient (HOG) descriptor for finger vein verification. Unlike MCM, EMC incorporates an enhancement mechanism to extract small vein delineation that is hardly visible in the extracted vein patterns. Next, HOG is applied instead of image binarization to convert a two-dimensional vein image into a one-dimensional feature vector for efficient matching. The HOG descriptor is able to characterize the local spatial representation of a finger vein by capturing the gradient information effectively. The proposed method is evaluated based on two datasets namely the PKU Finger Vein Database (V4) and SDUMLA-HMT finger vein database. Experiments show promising verification results with equal error rates as low as 0.33 % for DB1 and 0.14 % for DB2 respectively, when EMC+HOG+SVM is applied.
AB - Maximum Curvature Method (MCM) is one of the promising methods for finger vein verification. MCM scans the curvature of the vein image profiles within a finger for feature extraction. However, the quality of the image can be poor due to variations in illumination and sensor conditions. Furthermore, traditional MCM matching of the vein pattern requires extensive processing time. To address these limitations, we propose an integrated Enhanced Maximum Curvature (EMC) method with Histogram of Oriented Gradient (HOG) descriptor for finger vein verification. Unlike MCM, EMC incorporates an enhancement mechanism to extract small vein delineation that is hardly visible in the extracted vein patterns. Next, HOG is applied instead of image binarization to convert a two-dimensional vein image into a one-dimensional feature vector for efficient matching. The HOG descriptor is able to characterize the local spatial representation of a finger vein by capturing the gradient information effectively. The proposed method is evaluated based on two datasets namely the PKU Finger Vein Database (V4) and SDUMLA-HMT finger vein database. Experiments show promising verification results with equal error rates as low as 0.33 % for DB1 and 0.14 % for DB2 respectively, when EMC+HOG+SVM is applied.
UR - http://www.scopus.com/inward/record.url?scp=84959158119&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84959158119&partnerID=8YFLogxK
U2 - 10.1007/s11042-016-3315-4
DO - 10.1007/s11042-016-3315-4
M3 - Article
AN - SCOPUS:84959158119
VL - 76
SP - 6859
EP - 6887
JO - Multimedia Tools and Applications
JF - Multimedia Tools and Applications
SN - 1380-7501
IS - 5
ER -