Combined PCA-Daugman Method : An Effcient Technique for Face and Iris Recognition

Main Article Content

Md. Mahbubul Alam
Md. Ashikur Rahman Khan
Zayed Us Salehin
Main Uddin
Sultana Jahan Soheli
Tanvir Zaman Khan

Abstract

Face and iris are very common individual bio-metric features for person identification. Face recognition is the method of identification a person uniquely using face. Principal component analysis is one of the algorithms for face recognition. Iris recognition in another method of person identification using iris. Very popular iris recognition method is Daugman algorithm. Unimodal biometric system has various difficulties to detect a person like noisy and unusual data. Multimodal biometric system combined more than one individual modalities like face and iris to increase the efficiency. In this work, we combined principal component analysis and Daugman algorithm with ORL, YALE, CASIA and Real face dataset to combine face and iris recognition to improve the recognition efficiency.

Keywords:
Principal component analysis, daugman algorithm, face recognition, iris recognition, person identification.

Article Details

How to Cite
Alam, M. M., Khan, M. A. R., Salehin, Z. U., Uddin, M., Soheli, S. J., & Khan, T. Z. (2020). Combined PCA-Daugman Method : An Effcient Technique for Face and Iris Recognition. Journal of Advances in Mathematics and Computer Science, 35(5), 34-44. https://doi.org/10.9734/jamcs/2020/v35i530280
Section
Original Research Article

References

What is facial recognition? - Definition from techopedia. Techopedia.com.
Available: https://www.techopedia.com/definition/32071/facial-recognition.
(Accessed: 19-Mar-2020).

Ion Marques, Manuel Grana. Face recognition algorithms; 2010.

Min WY, Romanova E, Lisovec Y, San AM. Application of statistical data processing for solving the problem of face recognition by using principal components analysis method. 2019

IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), Saint Petersburg and Moscow, Russia. 2019;2208-2212.

Shermina J. Illumination invariant face recognition using discrete cosine transform and principal component analysis. 2011 International Conference on Emerging Trends in Electrical and Computer Technology, Nagercoil. 2011;826-830.

Sharkas M. A neural network based approach for iris recognition based on both eyes. 2016 SAI Computing Conference (SAI), London. 2016;253-258.

Chandranayaka IR. Various iris recognition algorithms for biometric identification: A review.

International Journal of Exploring Emerging Trends in Engineering (IJEETE). 2016;03(04):286 Jie Lin, Jian-Ping Li, Hui Lin, Ji Ming. Robust person identification with face and iris by modified PUM method. 2009 International Conference on Apperceiving Computing and Intelligence Analysis, Chengdu. 2009;321-324.

Jamdar SD, Golhar Y. Implementation of unimodal to multimodal biometrie feature level fusion of combining face iris and ear in multi-modal biometric system. 2017 International Conference on Trends in Electronics and Informatics (ICEI), Tirunelveli. 2017;625-629.

Bouzouina Y, Hamami L. Multimodal biometric: Iris and face recognition based on feature selection of iris with GA and scores level fusion with SVM. 2017 2nd International Conference on Bio-engineering for Smart Technologies (BioSMART), Paris. 2017;1-7.

Ko T. Multimodal biometric identification for large user population using fingerprint, face and iris recognition. 34th Applied Imagery and Pattern Recognition Workshop (AIPR’05), Washington, DC. 2005;6-223.

Azom V, Adewumi A, Tapamo J. Face and Iris biometrics person identification using hybrid fusion at feature and score-level. 2015 Pattern Recognition Association of South Africa and Robotics and Mechatronics International Conference (PRASA-RobMech), Port Elizabeth.
;207-212.

Dakre VV, Gawande PG. An efficient technique of multimodal biometrics using fusion of face and iris features. 2016 Conference on Advances in Signal Processing (CASP), Pune. 2016;231- Milad Soltan, Saeid Toosi Zadeh, Hamid-Reza Pourreza. Daugmans algorithm enhancement for iris localization. Advanced Materials Research. 2012;403-408:3959-3964.

Prateek Verma, Maheedhar Dubey, Praveen Verma, Somak Basu. Daughmans algorithmmethod for iris recognition-a biometric approach. International Journal of Emerging Technology and Advanced Engineering. 2012;2(6).

Dinkova P, Georgieva P, Manolova A, Milanova M. Face recognition based on subject dependent Hidden Markov Models. 2016 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom), Varna. 2016;1-5.

Agarwal M, Agrawal H, Jain N, Kumar M. Face recognition using principle component analysis, eigenface and neural network. 2010 International Conference on Signal Acquisition and Processing, Bangalore. 2010;310-314.

Shailaja K, Anuradha B. Effective face recognition using deep learning based linear discriminant classification. 2016 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), Chennai. 2016;1-6.

Abuzneid MA, Mahmood A. Enhanced human face recognition using LBPH descriptor, multiknn, and back-propagation neural network. In IEEE. 2018;6:20641-20651.

UCSD computer vision. Yale Face Database.
Available: http://vision.ucsd.edu/content/yale-face-database
(Accessed: 07-Mar-2020).

Biometrics ideal test.
Available: http://biometrics.idealtest.org/dbDetailForUser.do?id=1
(Accessed: 21-Apr-2020).