Volume 5, Issue 2 (February 2018), Pages: 103-107

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 Original Research Paper

 Title: A combined score level fusion approach for multi model biometric system using left and right palm print

 Author(s): B. Baron Sam *, M. Saravanan

 Affiliation(s):

 School of Computing, Sathyabama University, Chennai, India

 https://doi.org/10.21833/ijaas.2018.02.017

 Full Text - PDF          XML

 Abstract:

Biometrics is the art of setting up the character of an individual in view of the physical, concoction or behavioral properties of the individual. Palm Print recognizable proof is an imperative individual ID innovation and it has pulled in much consideration. The palm print contains standard bends and wrinkles as well as rich surface and miniscule focuses, so the palm print ID can accomplish a high exactness. We propose a novel structure of joining the left with right palm print at the coordinating (matching) score level. In the system, three sorts of coordinating (matching) scores, which are separately acquired by the left palm print coordinating, right palm print coordinating and crossing coordinating between the left query and right training palm print, are fused to make the final decision. The structure not only combines the left and right palm print images for identification, additionally appropriately abuses the comparability between the left and right palm print of a similar subject. The proposed strategy accurately takes the method for the left and right palm print pictures into record, and plans estimation to survey the relation between them. In additament, by using this nearness, the proposed weighted cumulation scheme uses a system to facilitate the three sorts of scores incited from the left and right palm print pictures. 

 © 2017 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords: Biometrics, Palm print, Matching score

 Article History: Received 1 August 2017, Received in revised form 2 December 2017, Accepted 10 December 2017

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2018.02.017

 Citation:

 Sam BB and Saravanan M (2018). A combined score level fusion approach for multi model biometric system using left and right palm print. International Journal of Advanced and Applied Sciences, 5(2): 103-107

 Permanent Link:

 http://www.science-gate.com/IJAAS/2018/V5I2/Sam.html

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 References (13)

1. Ananth C, Gayathri G, Barvin MM, Parsana NJ, and Banu MP (2014b). Image Segmentation by Multi-shape GC-OAAM. American Journal of Sustainable Cities and Society (AJSCS), 1(3): 274-280. 
2. Ananth C, Gayathri G, Sankari IU, Vidhya A, and Karthiga P (2014a). Automatic image segmentation method based on ALG. International Journal of Innovative Research in Computer and Communication Engineering (IJIRCCE), 2(4): 3716-3721.     
3. Dai J and Zhou J (2011). Multifeature-based high-resolution palmprint recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(5): 945-957. https://doi.org/10.1109/TPAMI.2010.164  PMid:20733215 
4. Du F, Yu P, Li H, and Zhu L (2011). Palmprint recognition using Gabor feature-based bidirectional 2DLDA. Computer Science for Environmental Engineering and EcoInformatics, Springer, Berlin, Heidelberg: 230-235. https://doi.org/10.1007/978-3-642-22691-5_40 
5. Huang DS, Jia W, and Zhang D (2008). Palmprint verification based on principal lines. Pattern Recognition, 41(4): 1316-1328. https://doi.org/10.1016/j.patcog.2007.08.016 
6. Jain AK and Ross A (2004). Multibiometric systems. Communications of the ACM, 47(1): 34-40. https://doi.org/10.1145/962081.962102 
7. Jain AK, Flynn P, and Ross AA (2007). Handbook of biometrics. Springer Science and Business Media, Berlin, Germany.     
8. Kong AK and Zhang D (2004). Competitive coding scheme for palmprint verification. In the 17th International Conference on Pattern Recognition, IEEE, Cambridge, UK, 1: 520-523. https://doi.org/10.1109/ICPR.2004.1334184 
9. Palanikumar P, Shirly SG, and Balakrishnan S (2015). An effective two way classification of breast cancer images. International Journal of Applied Engineering Research, 10(21): 42472-42475.     
10. Ribaric S and Fratric I (2005). A biometric identification system based on eigenpalm and eigenfinger features. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(11): 1698-1709. https://doi.org/10.1109/TPAMI.2005.209  PMid:16285370 
11. Wu X, Zhao Q, and Bu W (2014). A SIFT-based contactless palmprint verification approach using iterative RANSAC and local palmprint descriptors. Pattern Recognition, 47(10): 3314-3326. https://doi.org/10.1016/j.patcog.2014.04.008 
12. Xu Y, Fei L, and Zhang D (2015). Combining left and right palmprint images for more accurate personal identification. IEEE Transactions on Image Processing, 24(2): 549-559. https://doi.org/10.1109/TIP.2014.2380171  PMid:25532174 
13. Zhang D, Kong WK, You J, and Wong M (2003). Online palmprint identification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(9): 1041-1050. https://doi.org/10.1109/TPAMI.2003.1227981