Improving Human's Finger Knuckle Identification using High Order Zernike Moments

  • Mehran Emadi Department of Electrical Engineering, Mobarakeh Branch, Islamic Azad University, Mobarakeh, Isfahan, Iran.
  • Mansoor Jafar Pour Department of Electrical Engineering, Mobarakeh Branch, Islamic Azad University, Mobarakeh, Isfahan, Iran.
Keywords: Identification rate, Zernike, Moment, Human's Finger Knuckle Identification

Abstract

Identification systems based on biometrics have been conventional more than traditional identification systems in the last two decades. Many biometrics have been provided such as fingerprint, palm, iris, the vein of palm and veins of fingerprint and such. One of the challenges discussed in biometrics is physical damages. The biometric of fingers knuckles is one of the biometrics less exposed to the physical damages. Several methods have been suggested for identification with various weak points such as high mathematical complications and a very low rate of identification. The present study suggests a new method for identification which is based on Zernike Moment. Zernike moment extracts the features of the picture several times. What distinguishes this algorithm from its counterparts is that it has got high accuracy in demarcating similar pictures of different classifications. In addition to its logical calculating complications, this method was able to record a very appropriate rate of identification facing some challenges such as noise, rotation, and transition.  

References

[1] Bharadwaj, S., Vatsa, M., Singh, R. "Biometric quality: a review of fingerprint, iris, and face," EURASIP journal on Image and Video Processing,Vol. 2014, No. 1, p. 34, 2014.
[2] Wang, B., Chen, Q., Yang, L.T., Chao, H.-C. "Indoor smartphone localization via fingerprint crowdsourcing: challenges and approaches," IEEE Wireless Communications,Vol. 23, No. 3, pp. 82-89, 2016.
[3] Krishneswari, K., Arumugam, S. "A review on palm print verification system," International Journal of Computer Information Systems and Industrial Management Applications (IJCISIM) ISSN,No. pp. 2150-7988, 2010.
[4] Farokhi, S., Sheikh, U., Flusser, J., Shamsuddin, S., Hashemi, H. "Evaluating feature extractors and dimension reduction methods for near infrared face recognition systems," Jurnal Teknologi,Vol. 70, No. pp. 23-33, 2014.
[5] Kaur, N., Juneja, M. "A review on iris recognition," Engineering and Computational Sciences (RAECS), 2014 Recent Advances in, pp. 1-5.
[6] Zhang, L., Zhang, L., Zhang, D., Zhu, H. "Online finger-knuckle-print verification for personal authentication," Pattern recognition,Vol. 43, No. 7, pp. 2560-2571, 2010.
[7] Verma, G., Sinha, A. "Finger Knuckle Print Recognition Based on Wavelet and Gabor Filtering," Proceedings of International Conference on Computer Vision and Image Processing, pp. 35-45.
[8] Verma, G., Sinha, A. "Design of Advanced Correlation Filters for Finger Knuckle Print Authentication Systems," Proceedings of International Conference on Computer Vision and Image Processing, pp. 47-56.
[9] Bhattacharya, N., Dewangan, D.K., Dewangan, K.K., An Efficacious Matching of Finger Knuckle Print Images Using Gabor Feature, ICT Based Innovations, Springer2018, pp. 153-162.
[10] Ross, A., Jain, A. "Information fusion in biometrics," Pattern recognition letters,Vol. 24, No. 13, pp. 2115-2125, 2003.
[11] Neware, S., Mehta, K., Zadgaonkar, A. "Finger knuckle identification using principal component analysis and nearest mean classifier," International Journal of Computer Applications,Vol. 70, No.2013.,9.
[12] Kong, W.K., Zhang, D. "Palmprint texture analysis based on low-resolution images for personal authentication," Pattern Recognition, 2002. Proceedings. 16th International Conference on, pp. 807-810.
[13] Kong, A., Zhang, D., Kamel, M. "A study of brute-force break-ins of a palmprint verification system," International Conference on Audio-and Video-Based Biometric Person Authentication, pp. 447-454.
[14] Yin, J., Zhou, J., Jin, Z., Yang, J. "Weighted linear embedding and its applications to finger-knuckle-print and palmprint recognition," Emerging Techniques and Challenges for Hand-Based Biometrics (ETCHB), 2010 International Workshop on, pp. 1-4.
[15] Zhang, L., Zhang, L., Zhang, D. "Monogeniccode: A novel fast feature coding algorithm with applications to finger-knuckle-print recognition," Emerging Techniques and Challenges for Hand-Based Biometrics (ETCHB), 2010 International Workshop on, pp. 1-4.
[16] Zhao, R., Li, K., Liu, M., Sun, X. "A novel approach of personal identification based on single knuckleprint image," Information Processing, 2009. APCIP 2009. Asia-Pacific Conference on, pp. 218-221.
[17] Zhang, L., Personal authentication using finger-knuckle-print, Hong Kong Polytechnic University (People's Republic of China), 2011.
[18] Farokhi , S., Sheikh, U.U., Flusser, J., Yang, B. "Near infrared face recognition using Zernike moments and Hermite kernels," Information Sciences, Vol. 316, No. pp. 234-245, 2015.
[19] Avazpour, N., Emadi , M. "Iris recognition methods: A review," International Journal of Computer Science and Information Security, Vol. 14, No.11. pp. 527-534.
[20] Avazpour, N., Emadi , M. "Optimization of human recognition from the Iris Images using the Haar wavelet," Majlesi Journal of Telecommunication Devices, Mar 2119, Vol. 8, Issue 1. pp. 13-19.
Published
2020-03-01
How to Cite
Emadi, M., & Jafar Pour, M. (2020). Improving Human’s Finger Knuckle Identification using High Order Zernike Moments. Majlesi Journal of Electrical Engineering, 14(1), 119-125. Retrieved from http://www.mjee.org/index/index.php/ee/article/view/3436
Section
Articles