Dane Brown
ABSTRACT
The feature-level, unlike the match score-level, lacks multi-modal fusion guidelines. This work demonstrates a practical approach for improved image-based biometric feature-fusion. The approach extracts and combines the face, fingerprint and palmprint at the feature-level for improved human identification accuracy. Feature-fusion guidelines, proposed in recent work, are extended by adding the palmprint modality and the support vector machine classifier. Guidelines take the form of strengths and weaknesses as observed in the applied feature processing modules during preliminary experiments. The guidelines are used to implement an effective biometric fusion system at the feature-level to reduce the equal error rate on the SDUMLA and IITD datasets, using a novel feature-fusion methodology.
- M. I. Ahmad, W. L. Woo, and S. S. Dlay. Multimodal biometric fusion at feature level: Face and palmprint. In 2010 7th International Symposium on Communication Systems Networks and Digital Signal Processing (CSNDSP), pages 801--805. IEEE, 2010.Google Scholar
Cross Ref
- T. Ahonen, A. Hadid, and M. Pietikainen. Face description with local binary patterns: Application to face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(12):2037--2041, 2006. Google ScholarDigital Library
- P. N. Belhumeur, J. P. Hespanha, and D. Kriegman. Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7):711--720, 1997. Google ScholarDigital Library
- A. C. Bovik. Handbook of image and video processing. Academic Press, 2010. Google ScholarDigital Library
- D. Brown. Faster upper body pose recognition and estimation using Compute Unified Device Architecture. PhD thesis, University of Western Cape, 2013.Google Scholar
- A. Buades, B. Coll, and J.-M. Morel. A non-local algorithm for image denoising. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005. CVPR 2005., volume 2, pages 60--65. IEEE, 2005. Google ScholarDigital Library
- G. S. Budhi, R. Adipranata, and F. J. Hartono. The use of Gabor filter and back-propagation neural network for the automobile types recognition. In 2nd International Conference SIIT 2010, 2010.Google Scholar
- S. Chikkerur, A. N. Cartwright, and V. Govindaraju. Fingerprint enhancement using STFT analysis. Pattern Recognition, 40(1):198--211, 2007. Google ScholarDigital Library
- A. Deshmukh, S. Pawar, and M. Joshi. Feature level fusion of face and fingerprint modalities using Gabor filter bank. In 2013 IEEE International Conference on Signal Processing, Computing and Control (ISPCC), pages 1--5. IEEE, 2013.Google ScholarCross Ref
- J. Feng and A. Jain. Fingerprint reconstruction: From minutiae to phase. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(2):209--223, Feb 2011. Google ScholarDigital Library
- O. N. Iloanusi. Fusion of finger types for fingerprint indexing using minutiae quadruplets. Pattern Recognition Letters, 38(0):8--14, 2014. Google ScholarDigital Library
- A. K. Jain, S. Prabhakar, L. Hong, and S. Pankanti. Filterbank-based fingerprint matching. IEEE Transactions on Image Processing, 9(5):846--859, 2000. Google ScholarDigital Library
- M. V. Karki and S. S. Selvi. Multimodal biometrics at feature level fusion using texture features. International Journal of Biometrics and Bioinformatics, 7(1):58--73, 2013.Google Scholar
- D. Kaur and G. Kaur. Level of fusion in multimodal biometrics: A review. International Journal of Advanced Research in Computer Science and Software Engineering, 3(2):242--246, 2013.Google Scholar
- W. Li, B. Zhang, L. Zhang, and J. Yan. Principal line-based alignment refinement for palmprint recognition. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 42(6):1491--1499, 2012. Google ScholarDigital Library
- D. Maltoni, D. Maio, A. K. Jain, and S. Prabhakar. Handbook of fingerprint recognition. Springer Science & Business Media, 2009. Google ScholarDigital Library
- D. Peralta, I. Triguero, R. Sanchez-Reillo, F. Herrera, and J. Benitez. Fast fingerprint identification for large databases. Pattern Recognition, 47(2):588--602, Feb 2014. Google ScholarDigital Library
- P. Porwik and K. Wrobel. The new algorithm of fingerprint reference point location based on identification masks. In Computer Recognition Systems, pages 807--814. Springer, 2005.Google Scholar
- R. Raghavendra, B. Dorizzi, A. Rao, and G. H. Kumar. Designing efficient fusion schemes for multimodal biometric systems using face and palmprint. Pattern Recognition, 44(5):1076--1088, 2011. Google ScholarDigital Library
- A. Rattani, D. R. Kisku, M. Bicego, and M. Tistarelli. Feature level fusion of face and fingerprint biometrics. In Biometrics: Theory, Applications and Systems (BTAS), pages 1--5, 2011.Google Scholar
- S. Ribaric and I. Fratric. Experimental evaluation of matching-score normalization techniques on different multimodal biometric systems. In Electrotechnical Conference, MELECON 2006. IEEE Mediterranean, pages 498--501. IEEE, 2006.Google ScholarCross Ref
- F. S. Samaria and A. C. Harter. Parameterisation of a stochastic model for human face identification. In Proceedings of the Second IEEE Workshop on Applications of Computer Vision, pages 138--142. IEEE, 1994.Google ScholarCross Ref
- P. Sharma and M. Kaur. Multimodal classification using feature level fusion and SVM. International Journal of Computer Applications, 76(4):26--32, 2013.Google ScholarCross Ref
- Z. Wang, C. Liu, T. Shi, and Q. Ding. Face-palm identification system on feature level fusion based on CCA. Journal of Information Hiding and Multimedia Signal Processing, 4(4):272--279, 2013.Google Scholar
- Y.-F. Yao, X.-Y. Jing, and H.-S. Wong. Face and palmprint feature level fusion for single sample biometrics recognition. Neurocomputing, 70(7):1582--1586, 2007. Google ScholarDigital Library
- Y. Yin, L. Liu, and X. Sun. SDUMLA-HMT: a multimodal biometric database. In Biometric Recognition, pages 260--268. Springer, 2011. Google ScholarDigital Library
- J. Zou, J. Feng, X. Zhang, and M. Ding. Local orientation field based nonlocal means method for fingerprint image de-noising. Journal of Signal and Information Processing, 4:150, 2013.Google ScholarCross Ref
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