Enhanced maximum curvature descriptors for finger vein verification

Munalih Ahmad Syarif, Thian Song Ong, Andrew B.J. Teoh, Connie Tee

Research output: Contribution to journalArticlepeer-review

46 Citations (Scopus)


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 languageEnglish
Pages (from-to)6859-6887
Number of pages29
JournalMultimedia Tools and Applications
Issue number5
Publication statusPublished - 2017 Mar 1

Bibliographical note

Funding Information:
This research uses two finger vein databases, PKU Finger Vein Database and SDUMLA, provided by Artificial Intelligence Lab of Peking University and Shandong University, respectively. This research was supported by Science Fund MOSTI Malaysia under grants MMUE/130153.

Publisher Copyright:
© 2016, Springer Science+Business Media New York.

All Science Journal Classification (ASJC) codes

  • Software
  • Media Technology
  • Hardware and Architecture
  • Computer Networks and Communications


Dive into the research topics of 'Enhanced maximum curvature descriptors for finger vein verification'. Together they form a unique fingerprint.

Cite this