Name
Title | ||
|---|---|---|
Multi-orientation and multi-scale features discriminant learning for palmprint recognition. |
Abstract | ||
|---|---|---|
Palmprint contains stable and effective features, especially the orientation and scale features of palm lines, and has now become an important identity recognition technique for surveillance and safety applications. Existing palmprint recognition methods using texture features can be roughly divided into two categories: coding-based and local descriptor based methods. As compared with the latter category, the former one can make full use of the palmprint specific features and acquire fast matching speed. However, most existing coding-based methods are based on the competitive coding scheme, in which the scale features of palmprint cannot be well exploited. In this work, we propose a discriminant orientation and scale features learning (DOSFL) for palmprint recognition. By introducing the idea of discriminant analysis into palmprint coding, DOSFL can extract the orientation and scale features with more favorable discriminability. Then, DOSFL utilizes four code bits to represent both the orientation and scale features of palmprint, and employs the Hamming distance for code matching. To make better use of the orientation and scale information contained in palmprint samples, we further propose a multi-orientation and multi-scale features discriminant learning (MOSDL) approach for palmprint recognition, which can fuse different orientation and scale feature information effectively in the discriminant learning process. Experimental results on two publicly available palmprint databases, including the HK PolyU database and UST palmprint image database, demonstrate that our proposed approach can achieve better recognition results than the compared methods. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.neucom.2018.06.086 | Neurocomputing |
Keywords | Field | DocType |
Palmprint recognition,Multiple orientation,Scale features learning,Palmprint coding,Code bits | Pattern recognition,Discriminant,Coding (social sciences),Hamming distance,Artificial intelligence,Linear discriminant analysis,Image database,Mathematics,Identity recognition | Journal |
Volume | ISSN | Citations |
348 | 0925-2312 | 1 |
PageRank | References | Authors |
0.35 | 31 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fei Ma | 1 | 52 | 13.61 |
| Xiaoke Zhu | 2 | 78 | 7.77 |
| Cailing Wang | 3 | 20 | 2.08 |
| Huajun Liu | 4 | 139 | 11.42 |
| Xiao-Yuan Jing | 5 | 211 | 26.18 |