A new framework for iris and fingerprint recognition using svm classification and extreme learning machine based on score level fusion
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A new framework for iris and fingerprint recognition using svm classification and extreme learning machine based on score level fusion

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Two biometric characteristics are considered in this study: iris and fingerprint. ...

Two biometric characteristics are considered in this study: iris and fingerprint.

The score level fusion is used to combine the characteristics from different biometric modalities.
Fusion at the score level is a new technique, which has a high potential for efficient consolidation of multiple unimodal biometric matcher outputs.

Support vector machine and extreme learning techniques are used in this system for recognition of biometric traits.

The proposed method provides better performance. ELM provides better performance as compare to the SVM. It reduces the classification time of current system.

This work is valuable and makes an efficient accuracy in such applications. This system can be utilized for person identification in several applications.

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A new framework for iris and fingerprint recognition using svm classification and extreme learning machine based on score level fusion Presentation Transcript

  • 1. A New Framework for IRIS and Fingerprint Recognition Using SVM Classification and Extreme Learning Machine Based on Score Level Fusion
  • 2. Abstract • Two biometric characteristics are considered in this study: iris and fingerprint. • The score level fusion is used to combine the characteristics from different biometric modalities. – Fusion at the score level is a new technique, which has a high potential for efficient consolidation of multiple unimodal biometric matcher outputs. • Support vector machine and extreme learning techniques are used in this system for recognition of biometric traits. • The proposed method provides better performance. ELM provides better performance as compare to the SVM. It reduces the classification time of current system. • This work is valuable and makes an efficient accuracy in such applications. This system can be utilized for person identification in several applications.
  • 3. Objective • Establishing the identity of a person is a critical task in any identity management system. • Surrogate representations of identity such as passwords and ill cards are not sufficient for reliable identity determination because they can be easily misplaced, shared, or stolen. • Biometric recognition is the science of establishing the identity of a person using his/her anatomical and behavioral traits. • Our objective is to establish the identity of a person, in any identity management system.
  • 4. Software Requirements • Operating System : Windows XP • Language : MATLAB • Version : MATLAB 7.9
  • 5. Hardware Requirements • Pentium IV – 2.7 GHz • 1 GB DDR RAM • 250 GB Hard Disk
  • 6. Existing System • Minutiae and texture based fingerprint fusion study using a Quality-Weighted Sum (QWS) rule for score level fusion • Palm print recognition using rank level fusion. • Biometrics system using iris and face fusion is performed at matching score level using weighted scores. • Biometric system for face and hand using feature level fusion with PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis) method. • Multimodal approach for palmprint and hand geometry, with fusion methods at the feature level by combining the feature vectors by concatenation, and the matching score level by using max rule.
  • 7. Proposed System • Features are extracted from Fingerprint modality and iris and are fused individually with the Iris modality to further evaluate the fusion results. • The individual features of two traits, iris and fingerprint are combined at the matching score level to develop a multimodal biometric authentication system. • K-means clustering database. is used to searching the • Support vector machine and Extreme learning machine is used for recognition.
  • 8. Applications • Access control – – – – Access control to computer systems (workstations Door security Portable media: USB sticks & mobile hard-drives Safes with biometric locks • Time and attendance management – Avoids fooling – Reduces overhead for security personnel when badges are lost or pin-codes forgotten. • Surveillance • Visit program
  • 9. Conclusion • This work focuses on using the multimodal biometrics: A New framework for fingerprint and iris recognition using support vector machine based score level fusion. • The individual scores of two traits, iris and fingerprint are combined at the matching score level to develop a multimodal biometric authentication system. • K-means clustering is used to searching the database. • Comparison of Support vector machine and Extreme learning machine will decrease the recognition time. • The experiments are conducted to evaluate the performance of support vector machine and extreme learning machine. • Comparing the classification time perform Extreme learning machine better than the support vector machine. • The experimental results show that comparing SVM and ELM with K-mean cluster methods provide clustering score based on similarity done and reduce the classification time.
  • 10. Future Work • To employ the same feature extraction technique for iris also with few additional preprocessing steps such as histogram equalization, fast fourier transform, binarization, direction and thinning.
  • 11. /AvvenireTechnologies /avveniretech