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Texture Features to Evaluate Biometric Verification System using Handvein and Palmprint
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 180 - Number 52 |
| Year of Publication: 2018 |
| Authors: Shreyas Rangappa, Naveena C., H. K. Chethan, G. Hemantha Kumar |
10.5120/ijca2018917375
|
Shreyas Rangappa, Naveena C., H. K. Chethan, G. Hemantha Kumar . Texture Features to Evaluate Biometric Verification System using Handvein and Palmprint. International Journal of Computer Applications. 180, 52 ( Jun 2018), 37-41. DOI=10.5120/ijca2018917375
Abstract
In recent years multimodal biometrics plays vital role in real life scenarios. We have proposed evaluated a biometric verification system of universal acceptable hand based modalities. We have used the Dorsal Handvein and Palmprint traits that are recently emerging traits in the multimodal biometrics field. We used the well know texture methods like LBP, LPQ and Gabor filter to extract texture features on Handvein and Palm print databases. We compare results of texture methods individually and also we worked on combinations of all the features on both the modalities and which modality performs better on texture descriptors. We have shown results using GAR (Genuine Acceptance Rate) v/s FAR (False Acceptance Rate) with the threshold benchmark values of FAR (0.01%, 0.1%, 1%) to illustrate the performance of verification rate. At the last we have tested with Multi-algorithmic system to evaluate robustness of our system. From our experimental results, it is clearly evident that the LPQ+Gabor combination texture feature is more suitable for both modalities.
References
- Ali Younesi, Mehdi Chehel Amirani,: Gabor Filter and Texture Based Features for Palmprint Recognition, Iternational Conference on Computational Science, ICCS Procedia Computer Science, 2017.
- Shrevin Minaee, Yao Wang,: Palmprint Recognition using Deep Scattering Convolution Network, 2016.
- Haipeng Chen,: An Efficient Palmprint Recognition method Based on Block Dominant Orientation Code, Elsevier 2015.
- Saiyed Umer, Bibhas Chandra Dhara, Bhabatosh Chanda,: A Novel Palmprint Recognition System using Patch Based Filter Response, IEEE International Conference on Identity, Security and Behavior Analysis(ISAB), 2017.
- Jan Svoboda, Jonathan Masci, Michael M. Bronstein,: Palmprint Recognition via discriminative index learning, International Conference on Pattern Recognition (ICPR), 2016.
- Lunke Fei, Bob Zhang, Yong Xu, Liping Yan,: Palmprint Recognition Using Neighboring Direction Indicator, IEEE Transactions on Human-Machine Systems, volume 46, 2016.
- Gen Li, Jaihie Kim,: Palmprint recognition with Local Micro-structure Tetra Pattern, Elsevier, Pattern Recognition 61, 2017, 29-46.
- Yue-Tong Luo , Lan-Ying Zhao , Bob Zhang , Wei Jia, Feng Xue, Jing-Ting Lu , Yi-Hai Zhu , Bing-Qing Xu,: Local Line Directional Pattern for palmprint recognition, Elsevier, Pattern Recognition 50, 2016, 26-44.
- Huang, D., Zhu, X., Wang, Y., Zhang, D.: Dorsal hand vein recognition via hierarchical combination of texture and shape clues. Neurocomputing 214(C), 815–828 (2016). ISSN 0925–2312
- Wang, J., Wang, G., Li, M., Wenkai, D.: Hand vein recognition based on PCET. Optik 127, 7663–7669 (2016)
- Rossan, I., Khan, M.H.M.: Impact of changing parameters when preprocessing dorsal hand vein pattern. Procedia Comput. Sci. 32, 513–520 (2014)
- Hu, Y.-P., Wang, Z.-Y., Yang, X.-P., Xue, Y.-M.: Hand vein recognition based on the connection lines of reference point and feature point. Infrared Phys. Technol. 62, 110–114 (2014)
- Badawi, A.M.: Hand vein biometric verification prototype: a testing performance and patterns similarity. IPCV 14, 3–9 (2006)
- Nigam, A., Tiwari, K., Gupta, P.: Multiple texture information fusion for finger knuckleprint authentication system. Neurocomputing 188, 190–205 (2016).
- Raghavendra, R., Imran, M., Rao, A., Hemantha Kumar, G.: Multimodal biometrics: analysis of handvein and palmprint combination used for person verification. In: 2010 3rd International Conference on Emerging Trends in Engineering and Technology (ICETET). IEEE (2010).
- Ojala, T., Pietik¨ainen, M., Harwood, D.: A comparative study of texture measures with classification based on feature distributions. Pattern Recogn. 29(1), 51–59 (1996)
- Ojansivu, V., Heikkila, J.: Proceedings of the 3rd International Conference on Image and Signal Processing, ICISP 2008, pp. 236–243 (2008)
- Guo, Z., Zhang, L., Zhang, D.: A completed modeling of local binary pattern operator for texture classification. IEEE Trans. Image Process. 19, 1657–1663 (2010).
- Lumini, A., Nann, L.: Overview of the combination of biometric matchers. Inf. Fusion 33, 71–85 (2017). Original Research Article.
- Khandelwal, C.S., Maheshewari, R., Shinde, U.B.: Review paper on applications of principal component analysis in multimodal biometrics system original research article. Procedia Comput. Sci. 92, 481–486 (2016).
- Nanni L, Lumini A. RegionBoost learning for 2D + 3D based face recognition. Pattern Recogn Lett 2007;28(15):2063–70.
- Shang X, Veldhuis R. Local absolute binary patterns as image preprocessing for grip-pattern recognition in smart gun. In: Proceedings of the first IEEE international conference on biometrics: theory, applications, and systems; 2007. p.1–6.
- Nanni L, Lumini A. Local binary patterns for a hybrid fingerprint matcher. Pattern Recogn 2008;11:3461–6.
- Nanni L, Lumini A. A reliable method for cell phenotype image classification. Artif Intell Med 2008;43(2):87–97.
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