A new framework for iris and fingerprint recognition using svm classification and extreme learning...
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![Page 1: A new framework for iris and fingerprint recognition using svm classification and extreme learning machine based on score level fusion](https://reader033.fdocuments.in/reader033/viewer/2022061205/548097c7b379593a2b8b5a7b/html5/thumbnails/1.jpg)
A New Framework for IRIS and Fingerprint Recognition
Using SVM Classification and Extreme Learning Machine Based on Score Level Fusion
![Page 2: A new framework for iris and fingerprint recognition using svm classification and extreme learning machine based on score level fusion](https://reader033.fdocuments.in/reader033/viewer/2022061205/548097c7b379593a2b8b5a7b/html5/thumbnails/2.jpg)
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.
![Page 3: A new framework for iris and fingerprint recognition using svm classification and extreme learning machine based on score level fusion](https://reader033.fdocuments.in/reader033/viewer/2022061205/548097c7b379593a2b8b5a7b/html5/thumbnails/3.jpg)
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.
![Page 4: A new framework for iris and fingerprint recognition using svm classification and extreme learning machine based on score level fusion](https://reader033.fdocuments.in/reader033/viewer/2022061205/548097c7b379593a2b8b5a7b/html5/thumbnails/4.jpg)
Software Requirements• Operating System : Windows XP
• Language : MATLAB
• Version : MATLAB 7.9
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Hardware Requirements• Pentium IV – 2.7 GHz
• 1 GB DDR RAM
• 250 GB Hard Disk
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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.
![Page 7: A new framework for iris and fingerprint recognition using svm classification and extreme learning machine based on score level fusion](https://reader033.fdocuments.in/reader033/viewer/2022061205/548097c7b379593a2b8b5a7b/html5/thumbnails/7.jpg)
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 is used to searching the database.
• Support vector machine and Extreme learning machine is used for recognition.
![Page 8: A new framework for iris and fingerprint recognition using svm classification and extreme learning machine based on score level fusion](https://reader033.fdocuments.in/reader033/viewer/2022061205/548097c7b379593a2b8b5a7b/html5/thumbnails/8.jpg)
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
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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.
![Page 10: A new framework for iris and fingerprint recognition using svm classification and extreme learning machine based on score level fusion](https://reader033.fdocuments.in/reader033/viewer/2022061205/548097c7b379593a2b8b5a7b/html5/thumbnails/10.jpg)
Future Work• To employ the same feature extraction
technique for iris also with few additional pre-processing steps such as histogram equalization, fast fourier transform, binarization, direction and thinning.
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