A multimodal biometric detection system via rotated histograms using hough lines
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| collectionurl | https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072 |
| date | 2015-03-16 10:22:05 |
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| id | 11605 |
| institution | UniSZA |
| internalnotes | A.K. Jain, A. Ross, & S. Prabhakar. 2004. An introduction to biometric recognition. IEEE Transactions on Circuits and Systems for Video Technology, Vol.14, 2004, 4-20. Summary of NIST Standards for Biometric Accuracy. 2002. Tamper Resistance and Interoperability. Teddy Ko Raytheon. 2005. Multimodal Biometric Identification for Large User Population Using Fingerprint, Face and Iris Recognition. IEEE, Applied Imaginary and Pattern Recognition Workshop (AIPR05). 0-7695-2479 Mohammad, I., Ashok, R., & Hemantha, K.. 2013. Multimodal Biometric Fusion of Face and Palmprint at Various Levels. IEEE, International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp.1793-1798 978-1-4673-6217-7/13/$31.00. J. Kittler & A. Hojjatoleslami. 1998. A weighted combination of classifiers employing shared and distinct representations. IEEE Proc. Computer Vision Pattern Recognition, pp. 924–929. Hanuma, M. 2011. Real-time Live Face Detection using Face Template Matching and DCT Energy Analysis. International Conference of Soft Computing and Pattern Recognition (SoCPaR),ISBN:978-1-45771196-1/1l/ $26.00@2011 IEEE, pp. 342-346, 2011. Shapiro, L. and Stockman, G. 2001. Computer Vision. Prentice-Hall, Inc. How the Hough Transform was invented. IEEE Signal Processing Magazine. November 2009, 1053-5888/09/$26.00©2009IEEE Duda, R. O. and P. E. Hart. 1972. Use of the Hough Transformation to Detect Lines and Curves in Pictures. Comm. ACM, Vol. 15, pp. 11–15. P. Porwik. (2007), The compact three stages method of the signature recognition. 6th International Conference on Computer Information Systems and Industrial Management Applications (CISIM'07) 0-7695-2894-5/07 $20.00. IEEE. Vitoantonio, B., Pasquale, C., & Giuseppe, M. (2008), “Extending Hough Transform to a Points’ Cloud for 3D-Face Nose-Tip Detection”. D.-S. Huang et al. (Eds.): ICIC , LNAI 5227, pp. 1200–1209. © Springer-Verlag Berlin Heidelberg. Mohamed, R., Haniza, Y., Puteh, Saad., Ali Y. M. S., & Abdulrahman, S. (2005), “Object Detection using Circular Hough Transform”, American Journal of Applied Sciences 2 (12): 1606-1609, 2005. ISSN 1546-9239. Aparecido, N. M., & Anil, K.. J. 2005. “Ridge-Based Fingerprint Matching Using Hough Transform”, Proceedings of the XVIII Brazilian Symposium on Computer Graphics and Image Processing ((SIBGRAPI’05) 1530-1834/05 $20.00 © IEEE). Qi-chuan, T., Quan, P., Yong-mei, C., & Quan-xue, G. 2004. “Fast Algorithm and Application of Hough Transform in Iris Segmentation”, IEEE, Proceedings of the Third International Conference on Machine Learning and Cybernetics, Shanghai. 0-7803-8403-2/04/$20.00. Howitt, D. and Cramer, D. 2008. Statistics in Psychology. Prentice Hall. 2008. Fatma, S. M., Azizah, A., & Suriayati, C. 2010. “Histogram Matching For Color Detection: A Preliminary Study”, IEEE,978-1-4244-6716-7/10/$26.00 Chris, S., & Toby, B. 2011. “Fundamentals of Digital Image Processing: A Practical approach with examples in Mat lab”, Wiley-Blackwell, pp. 63-69 and 271-272, ISBN 978 0 470 84472 4. Harsh, K.., & Alpesh, P. 2013. “Application of Hough Transfom And Sub-Pixel Edge Detection in 1-D Barcode Scanning”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering (IJAREEIE), Vol.2,Issue6,pp.2173-2184. © www. ijareeie. Com. Malaysia Multimedia University. MMU1 iris image database, 2004. http://pesona.mmu.edu.my/ ̃ccteo. FEI face image database. http:// fei. edu. br/ ~cet / face database.html. (College of Engineering, Pune) COEP Palm Print image Database http://www.coep. org.in/ index. php ?pid=367. FVC2004: Fingerprint Verification Competition 2004 http://bias.csr.unibo.it/fvc2004/. Drira H., B. B. Amor, M. Daoudi, and R. Slama, (2013), “3D Face Recognition under Expressions, Occlusions and Pose Variation, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 35, no. 9. Faltemier T. C. T, K. W. K Bowyer, and P. J. P Flynn, (2008) “A Region Ensemble for 3D Face Recognition” IEEE Trans. Information Forensics and Security, vol. 3, no. 1, pp. 62-73. Haar F. T. and R. C. R. Velkamp, (2010) “Expression Modelling for Expression-invariant Face Recognition,” Computers and Graphics, vol. 34, no. 3, pp. 231-241. Hose J. D, J. Colineau, C. Bichon, and B. Dorizzi, (2007), “Precise Localization of Landmarks on 3D Faces using Gabor Wavelets,” In BTAS, pp. 1-6. |
| originalfilename | 5864-01-FH02-FIK-15-02653.jpg |
| person | UniSZA Unisza unisza |
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| resourceurl | https://intelek.unisza.edu.my/intelek/pages/view.php?ref=11605 |
| spelling | 11605 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=11605 https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072 Restricted Document Article Journal UniSZA Unisza unisza image/jpeg inches 96 96 1414 15 15 780 2015-03-16 10:22:05 1414x780 5864-01-FH02-FIK-15-02653.jpg UniSZA Private Access A multimodal biometric detection system via rotated histograms using hough lines ARPN Journal of Engineering and Applied Sciences Several systems require full identification of a user, as any misclassification may deteriorate the performance of the entire system. Such systems must grant access only to the genuine user. For this reason, single biometrics becomes insufficient for authentication and identification. Consequently, the need for implementing highly integrated systems is necessary to promote security of such systems. At the same time, multi-biometric attracts much attention. The current study put forward a pioneering multimodal biometric detection approach using the principle of detecting lines through Hough Transform (HT). The images were converted in to histograms using histogram plot function. However, these histograms images were rotated by 30 degrees and HT functions were applied on the rotated histograms to detect the query biometric features. The new technique was tested on face, iris, palm and fingerprint. The final plot accomplished detection of whole biometric features with an average detection time of 4.506 seconds per individual. The new technique can be used to detect the aforementioned biometric traits using the same feature extraction algorithm at limited time, since each biometric trait’s dimensions was drastically reduced. The new system outperformed many methods in the literature reported using conventional detection methods. Hence, the modified algorithm is applicable in multi-biometrics detection prior to recognition especially where little computation and fast performance is highly demanded. 10 3 Asian Research Publishing Network Asian Research Publishing Network 1479-1485 A.K. Jain, A. Ross, & S. Prabhakar. 2004. An introduction to biometric recognition. IEEE Transactions on Circuits and Systems for Video Technology, Vol.14, 2004, 4-20. Summary of NIST Standards for Biometric Accuracy. 2002. Tamper Resistance and Interoperability. Teddy Ko Raytheon. 2005. Multimodal Biometric Identification for Large User Population Using Fingerprint, Face and Iris Recognition. IEEE, Applied Imaginary and Pattern Recognition Workshop (AIPR05). 0-7695-2479 Mohammad, I., Ashok, R., & Hemantha, K.. 2013. Multimodal Biometric Fusion of Face and Palmprint at Various Levels. IEEE, International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp.1793-1798 978-1-4673-6217-7/13/$31.00. J. Kittler & A. Hojjatoleslami. 1998. A weighted combination of classifiers employing shared and distinct representations. IEEE Proc. Computer Vision Pattern Recognition, pp. 924–929. Hanuma, M. 2011. Real-time Live Face Detection using Face Template Matching and DCT Energy Analysis. International Conference of Soft Computing and Pattern Recognition (SoCPaR),ISBN:978-1-45771196-1/1l/ $26.00@2011 IEEE, pp. 342-346, 2011. Shapiro, L. and Stockman, G. 2001. Computer Vision. Prentice-Hall, Inc. How the Hough Transform was invented. IEEE Signal Processing Magazine. November 2009, 1053-5888/09/$26.00©2009IEEE Duda, R. O. and P. E. Hart. 1972. Use of the Hough Transformation to Detect Lines and Curves in Pictures. Comm. ACM, Vol. 15, pp. 11–15. P. Porwik. (2007), The compact three stages method of the signature recognition. 6th International Conference on Computer Information Systems and Industrial Management Applications (CISIM'07) 0-7695-2894-5/07 $20.00. IEEE. Vitoantonio, B., Pasquale, C., & Giuseppe, M. (2008), “Extending Hough Transform to a Points’ Cloud for 3D-Face Nose-Tip Detection”. D.-S. Huang et al. (Eds.): ICIC , LNAI 5227, pp. 1200–1209. © Springer-Verlag Berlin Heidelberg. Mohamed, R., Haniza, Y., Puteh, Saad., Ali Y. M. S., & Abdulrahman, S. (2005), “Object Detection using Circular Hough Transform”, American Journal of Applied Sciences 2 (12): 1606-1609, 2005. ISSN 1546-9239. Aparecido, N. M., & Anil, K.. J. 2005. “Ridge-Based Fingerprint Matching Using Hough Transform”, Proceedings of the XVIII Brazilian Symposium on Computer Graphics and Image Processing ((SIBGRAPI’05) 1530-1834/05 $20.00 © IEEE). Qi-chuan, T., Quan, P., Yong-mei, C., & Quan-xue, G. 2004. “Fast Algorithm and Application of Hough Transform in Iris Segmentation”, IEEE, Proceedings of the Third International Conference on Machine Learning and Cybernetics, Shanghai. 0-7803-8403-2/04/$20.00. Howitt, D. and Cramer, D. 2008. Statistics in Psychology. Prentice Hall. 2008. Fatma, S. M., Azizah, A., & Suriayati, C. 2010. “Histogram Matching For Color Detection: A Preliminary Study”, IEEE,978-1-4244-6716-7/10/$26.00 Chris, S., & Toby, B. 2011. “Fundamentals of Digital Image Processing: A Practical approach with examples in Mat lab”, Wiley-Blackwell, pp. 63-69 and 271-272, ISBN 978 0 470 84472 4. Harsh, K.., & Alpesh, P. 2013. “Application of Hough Transfom And Sub-Pixel Edge Detection in 1-D Barcode Scanning”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering (IJAREEIE), Vol.2,Issue6,pp.2173-2184. © www. ijareeie. Com. Malaysia Multimedia University. MMU1 iris image database, 2004. http://pesona.mmu.edu.my/ ̃ccteo. FEI face image database. http:// fei. edu. br/ ~cet / face database.html. (College of Engineering, Pune) COEP Palm Print image Database http://www.coep. org.in/ index. php ?pid=367. FVC2004: Fingerprint Verification Competition 2004 http://bias.csr.unibo.it/fvc2004/. Drira H., B. B. Amor, M. Daoudi, and R. Slama, (2013), “3D Face Recognition under Expressions, Occlusions and Pose Variation, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 35, no. 9. Faltemier T. C. T, K. W. K Bowyer, and P. J. P Flynn, (2008) “A Region Ensemble for 3D Face Recognition” IEEE Trans. Information Forensics and Security, vol. 3, no. 1, pp. 62-73. Haar F. T. and R. C. R. Velkamp, (2010) “Expression Modelling for Expression-invariant Face Recognition,” Computers and Graphics, vol. 34, no. 3, pp. 231-241. Hose J. D, J. Colineau, C. Bichon, and B. Dorizzi, (2007), “Precise Localization of Landmarks on 3D Faces using Gabor Wavelets,” In BTAS, pp. 1-6. |
| spellingShingle | A multimodal biometric detection system via rotated histograms using hough lines |
| summary | Several systems require full identification of a user, as any misclassification may deteriorate the performance of the entire system. Such systems must grant access only to the genuine user. For this reason, single biometrics becomes insufficient for authentication and identification. Consequently, the need for implementing highly integrated systems is necessary to promote security of such systems. At the same time, multi-biometric attracts much attention. The current study put forward a pioneering multimodal biometric detection approach using the principle of detecting lines through Hough Transform (HT). The images were converted in to histograms using histogram plot function. However, these histograms images were rotated by 30 degrees and HT functions were applied on the rotated histograms to detect the query biometric features. The new technique was tested on face, iris, palm and fingerprint. The final plot accomplished detection of whole biometric features with an average detection time of 4.506 seconds per individual. The new technique can be used to detect the aforementioned biometric traits using the same feature extraction algorithm at limited time, since each biometric trait’s dimensions was drastically reduced. The new system outperformed many methods in the literature reported using conventional detection methods. Hence, the modified algorithm is applicable in multi-biometrics detection prior to recognition especially where little computation and fast performance is highly demanded. |
| title | A multimodal biometric detection system via rotated histograms using hough lines |
| title_full | A multimodal biometric detection system via rotated histograms using hough lines |
| title_fullStr | A multimodal biometric detection system via rotated histograms using hough lines |
| title_full_unstemmed | A multimodal biometric detection system via rotated histograms using hough lines |
| title_short | A multimodal biometric detection system via rotated histograms using hough lines |
| title_sort | multimodal biometric detection system via rotated histograms using hough lines |