Name
Abstract | ||
|---|---|---|
This paper presents a reliable and robust palmprint verification approach that involves using a bi-feature, biometric, palmprint feature-point number (FPN) and a histogram of oriented gradient (HOG). The bi-feature was fused and verified using a support vector machine (SVM) at the feature level. The approach has the advantages of capturing palm images in pegless scenarios with a low cost and low-resolution (100 dpi) digital scanner, and one sensor can capture palmprint bi-feature information. The low-resolution images result in a smaller database. Nine thousand palmprint images were collected from 300 people to verify the validity of the proposed approach. The results showed an accurate classification rate of 99.04%. The experimental results demonstrated that the proposed approach is feasible and effective in palmprint verification. Our findings will help extend palmprint verification technology to security access control systems. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1142/S0218001413560077 | INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE |
Keywords | Field | DocType |
Palmprint verification, support vector machine, bifeature, multiresolution representation, histogram of oriented gradient | Computer vision,Histogram,Pattern recognition,Computer science,Support vector machine,Scanner,Access control,Artificial intelligence,Biometrics,Classification rate | Journal |
Volume | Issue | ISSN |
27 | 5 | 0218-0014 |
Citations | PageRank | References |
0 | 0.34 | 2 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chih-lung Lin | 1 | 493 | 36.54 |
| Hsu-Yung Cheng | 2 | 243 | 23.56 |
| Kuo-chin Fan | 3 | 1369 | 117.82 |
| Chun-Wei Lu | 4 | 7 | 1.49 |
| Chang-Jung Juan | 5 | 4 | 2.22 |
| Chih-Wei Kuo | 6 | 1 | 1.05 |