A Review in Various Approaches of Feature Extraction and Feature Fusion in Multimodal Biometric System

Authors

  • Deep Kumar  Software Engineer, Igniva Solutions Private Limited, Mohali, Punjab, India

Keywords:

PCA, Minutiae, GA, FFT and Gabo filter

Abstract

Biometric system is the field of digital image processing that has been used in various applications of security and surveillance. In the process of biometric authentication various approaches have been used for biometric traits based matching. Face, finger, iris and voice are the biometric traits that can be used in the process of biometric recognition system. Due to forgery various attackers are capable of forging biometric sample that allow them to break security available at any application. To overcome this issue multimodal biometric system based concept has been introduced. This process use combination of at least two biometric traits features so that better security system can be developed that can used under those application where high security must be required. In this paper various approaches have been studied that has been used for feature extraction andfeature fusion.

References

  1. Jiali Yu, Chi sheng Li: “Face Recognition Based on Euclidean Distance and Texture Features”. International Conference on Computational and Information Sciences, 2013, pp 56-60.
  2. Mohanaiah, P. Sathyanarayana, L. Guru Kumar: “Image Texture Feature Extraction Using GLCM Approach” International Journal of Scientific and Research Publications, May 2013 ISSN 2250-3153.
  3. KoneruAnuradha, Manoj Kumar Tyagi “A Novel Method of Face Recognition Using LBP,LTP And Gabor Features”, International Journal Of Scientific & Technology Research, June 2012 ISSN 2277-8616.
  4. Young Ho Park “A Multimodal Biometric Recognition of Touched Fingerprint and Finger-Vein” International Conference on Multimedia and Signal Processing (CMSP), 2011, vol. 1, pp. 247 – 250.
  5. Mobarakeh, A.K,Rizi, S.M. Khaniabadi, S.M. ; Bagheri, M.A., “Applying Weighted K-nearest centric neighbor as classifier to improve the finger vein recognition performance” IEEE International Conference on Control System, Computing and Engineering (ICCSCE), 2012, pp. 56 – 59.
  6. Muhammad Faisal Zafar, Zaigham Zaheer, lavaid Khurshid “Novel Iris Segmentation and Recognition System for Human Identification”, 978-1-4673-4426-5, IEEE, 2013.
  7. Zhonghua Lin “A novel iris recognition method based on the natural-open eyes” 978-1-4244-5897-4, 1090 – 1093, IEEE, 2010.
  8. Thumwarin, P “Iris recognition based on dynamic radius matching of iris image” 6786-6754, 1234-8765, IEEE, 2009.
  9. Demirel, Hasan, Ozcinar, Cagri and Gholamreza Anbarjafi (2010) “Satellite Image Contrast Enhancement Using Discrete Wavelet Transform and Singular Value Decomposition”, IEEE Geoscience And Remote Sensing Letters, Vol. 7, No. 2.
  10. Wei Jin, Bin Li and Ming You (2012) “Feature Extraction Based on Equalized ULBP for Face Recognition”,International Conference on Computer Science and Electronics Engineering, Vol. 2, pp. 532-536, ISBN 978-1-4673-0689-8.
  11. Jiande Sun, Caiming Zhang and Hua Yan (2012) “Low-Resolution Face Recognition with Variable Illumination Based on Differential Images” International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP), pp. 146-149, ISBN 978-1-4673-1741-2.
  12. Dong-Ju Kim, Sang Heon Lee and Myoung Kyu Sohn (2013) “Face Recognition with Local Directional Patterns”, International Journal of Security and Its Applications Vol. 7, No. 2.

Downloads

Published

2017-05-30

Issue

Section

Research Articles

How to Cite

[1]
Deep Kumar, " A Review in Various Approaches of Feature Extraction and Feature Fusion in Multimodal Biometric System, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 3, Issue 3, pp.734-739, May-June-2017.