View stunning SlideShares in full-screen with the new iOS app!Introducing SlideShare for AndroidExplore all your favorite topics in the SlideShare appGet the SlideShare app to Save for Later — even offline
View stunning SlideShares in full-screen with the new Android app!View stunning SlideShares in full-screen with the new iOS app!
Biometrics sensor image fusion refers to a process that fuses multispectral images captured at different resolutions and by different biometric sensors to acquire richer and complementary information to produce a new fused image in spatially enhanced form.
The fused image depicts spatially enhanced information of one or more biometric characteristics that is more understandable for human perception.
Biometrics image fusion at higher abstraction level (i.e., low-level) removes several inconsistencies, less relevant edge artifacts and noise in the fused images.
Uni-biometric systems: when a single biometric system uses for verification or identification of acquired biometrics characteristic, it is called uni-biometrics system (face, fingerprint, palmprint, etc.).
Multi-biometric systems: when more than one biometric traits use for identification or verification by fusion of those traits, then it is called multimodal biometrics (face and fingerprint, face and iris, etc).
The experiment is conducted on IITK multimodal database of face and palmprint images the multimodal database consists of 400 face images and 400 palmprint images of 200 individuals.
In these evidence fusion, different wavelet fusion rules are applied, namely, ‘maximum’, ‘UD’, ‘DU’ and “mean” fusion rules.
Multisensor biometric fusion based on ‘maximum’ fusion rule produces 98.81% accuracy, while biometric fusion based on ‘mean’ fusion rule, fusion based on ‘DU’ fusion rule, and fusion based on ‘UD’ fusion rule produce 97.43%, 96.27% and 89.93% accuracies, respectively, as shown in the ROC curve.
Contd… Figure. Performances are shown through ROC curves determined from different wavelet based fusion techniques. The fusion rules are – “Down-up (DU)” wavelet fusion rule, “Maximum” wavelet fusion rule, “Mean” wavelet fusion rule and “Up-down (UD)” wavelet fusion rule
In this paper, multisensor biometric image fusion scheme has been addressed for multibiometric user authentication.
The proposed technique efficiently minimizes the less irrelevant distinct variability and inconsistencies exist in the different biometric modalities and their characteristics by performing fusion of biometrics images at low-level.
The result shows that the proposed method exploits at the sensor level is robust, computationally efficient and less sensitive to unwanted noise, which confirms the validity and efficacy of the system
D. G. Lowe, “Distinctive image features from scale invariant keypoints,” International Journal of Computer Vision , vol. 60, no. 2, 2004.
U. Park, S. Pankanti, and A. K. Jain, " Fingerprint verification using SIFT features ," Proceedings of SPIE Defense and Security Symposium , 2008.
A. Rattani, D. R. Kisku, M. Bicego, and M. Tistarelli, “Robust feature-level multibiometric classification,” Proceedings of the Biometric Consortium Conference – A special issue in Biometrics, pp. 1- 6, 2006.
D. R. Kisku, A. Rattani, E. Grosso, and M. Tistarelli, “Face identification by SIFT-based complete graph topology”, Proceedings of the IEEE Workshop on Automatic Identification Advanced Technologies, 2007, pp. 63 – 68.
H. Yaghi, and H. Krim, “Probabilistic graph matching by canonical decomposition”, Proceedings of the International Conference on Image Processing, 2008, pp. 2368 – 2371.
R. Sitaraman, and A. Rosenfield, “Probabilistic analysis of two stage matching”, Pattern Recognition, vol. 22, no. 3, pp. 331 – 343, 1989.
L. S. Davis, “Shape matching using relaxation techniques,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 1, no. 1, pp. 60-72, Jan. 1979.
A. Rattani, D. R. Kisku, M. Bicego, and M. Tistarelli, “Feature level fusion of face and fingerprint biometrics”, Proceedings of the Biometrics: Theory, Applications and Systems, 2007.
C. Hsu, and R. Beuker, “Multiresolution feature-based image registration”, Proceedings of the Visual Communications and Image Processing, 2000, pp. 1 – 9.
A. K. Jain, A. Ross, and S. Prabhakar, “An introduction to biometrics recognition”, IEEE Transactions on Circuits and Systems for Video Technology , vol. 14, no. 1, pp. 4 – 20, 2004.
A. K. Jain, A. Ross, and S. Pankanti, “Biometrics: A tool for information security”, IEEE Transactions on Information Forensics and Security , vol. 1, no. 2, pp. 125 – 143, 2006.