Improving face recognition by elman neural network using curvelet transform and HSI color space

Ahmed S. S. Abdullah, Majida Ali Abed, Israa Al Barazanchi

Abstract


In this paper, a suggested algorithm was proposed to increase the efficiency of the Elman neural algorithm in face recognition. The proposed algorithm was studied on the images of 20 students from the Department of Computer Science, Tikrit University. First step creates dataset of faces, second step convert color space to HSI and using saturation layer, image decomposition using curvelet transform, feature extraction using Principle component analysis, and final step face recognition using Elman neural network. after applying proposed algorithm, the rate of face recognition 94%.

Keywords


face recognition, image processing, color space, neural network, curve let transform.

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References

L. M. Dou, Z. L. Mu, Z. L. Li, A. Y. Cao, and S. Y. Gong, “Research progress of monitoring, forecasting, and prevention of rockburst in underground coal mining in China,” International Journal of Coal Science & Technology, vol. 1, no. 3, pp. 278–288, 2014.

Tianwei Lan, Chaojun Fan, Sheng Li, Hongwei Zhang, and AndrianSergaevichBatugin, “Probabilistic Prediction of Mine Dynamic Disaster Risk Based on Multiple Factor Pattern Recognition,” Advances in Civil Engineering, vol. 2018

Kun Sun, Xin Yin, Mingxin Yang, Yang Wang, and Jianying Fan, “The Face Recognition Method Based on CS-LBP and DBN,” Mathematical Problems in Engineering, 2018,

IssamDagher and Hussein Al-Bazzaz, “Improving the Component-Based Face Recognition Using Enhanced Viola–Jones and Weighted Voting Technique,” Modelling and Simulation in Engineering, 2019.

J. J. Richler, O. S. Cheung, and I. Gauthier, “Holistic processing predicts face recognition,” Psychological Science, 2011.

R.Shyam and Y. N. Singh, “A taxonomy of 2D and 3D face recognition methods,” in Proceedings of the 1st International Conference on Signal Processing and Integrated Networks (SPIN '14), pp. 749–754, IEEE, Noida, India, February 2014

RadheyShyam and Yogendra Narain Singh, “Multialgorithmic Frameworks for Human Face Recognition,” Journal of Electrical and Computer Engineering, vol. 2016

AliHussien Mary," Face Recognition Based Wavelet-PCA Features and Skin Color Model" Journal of Engineering and Development,2011.

Maysaa Hameed Al-Hamdani," Face Image Recognition Using 2D PCA Algorithm" Eng. & Tech. Journal,2012

Abbas HussienMiry," Face Recognition Based Principal Component Analysis andWavelet Sub bands", Journal of Engineering and Development,2013

Anmar Ali Mohammad,"Enhancethe PCA Method to Strength Face Recognition Systems" Iraq journal of information technology .2014.

NashwanAlsalamAli," Face Recognition Using Stationary wavelet transform and Neural Network with Support Vector Machine ", Iraqi Journal of Science, 2015

M. M. Mukhedkar and S. B. Powalkar, "Fast face recognition based on Wavelet Transform on PCA," International Conference on Energy Systems and Applications, Pune, 2016

Fatma Zohra Chelali and Amar Djeradi, “Face Recognition Using MLP and RBF Neural Network with Gabor and Discrete Wavelet Transform Characterization: A Comparative Study,” Mathematical Problems in Engineering, vol. 2015

KanokmonRujirakul, Chakchai So-In, and BancharArnonkijpanich, “PEM-PCA: A Parallel Expectation-Maximization PCA Face Recognition Architecture,” The Scientific World Journal, vol. 2014

IssamDagher and Hussein Al-Bazzaz, “Improving the Component-Based Face Recognition Using Enhanced Viola–Jones and Weighted Voting Technique,” Modelling and Simulation in Engineering, vol. 2019

L.Boubchir and J. Fadili, “Multivariate statistical modelling of images with the curvelet transform,” in Proceedings of the 8th International Conference on Signal Processing, Pattern Recognition, and Applications, pp. 747–750, 2005

.Khalil I. Alsaif and Esraa S. Hussein," Curvelet Transform for Kidney Stones Detection", International Journal of Computer Science and Information Security, Vol. 15, No. 1, 2017.pp335-341

Lei Sun, Wenjun Yi, Dandan Yuan, and Jun Guan, “Application of Elman Neural Network Based on Genetic Algorithm in Initial Alignment of SINS for Guided Projectile,” Mathematical Problems in Engineering, vol. 2019,

Jun Pi, Jiangbo Huang, and Long Ma, “Aeroengine Fault Diagnosis Using Optimized Elman Neural Network,” Mathematical Problems in Engineering, vol. 2017

HayrettinToylan and HilmiKuscu, “A Real-Time Apple Grading System Using Multicolor Space,” The Scientific World Journal, vol. 2014, pp1-12

Zhenmin Zhu, Ruichao Song, Hui Luo, Jun Xu, and Shiming Chen, “Color Calibration for Colorized Vision System with Digital Sensor and LED Array Illuminator,” Active and Passive Electronic Components, vol. 2016, pp1-16

B. Durakovic and Torlak, M., “Simulation and experimental validation of phase change material and water used as heat storage medium in window applications”, Journal of Materials and Environmental Sciences, vol. 8, no. 5, pp. 1837-1846, 2017.

R. Palalic, Durakovic, B., Brankovic, A., and Ridic, O., “Students' Entrepreneurial Orientation Intention, Business Environment, and Networking: Insights from Bosnia and Herzegovina”, Students' Entrepreneurial Orientation Intention, Business Environment, and Networking: Insights from Bosnia and Herzegovina, vol. 11, no. 4, pp. 240-255 , 2016.

B. Durakovic and Basic, H., “Continuous Quality Improvement in Textile Processing by Statistical Process Control Tools: A Case Study of Medium-Sized Company”, Periodicals of Engineering and Natural Sciences, vol. 1, no. 1, pp. 39-47, 2013.




DOI: http://dx.doi.org/10.21533/pen.v7i2.485

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Copyright (c) 2019 Ahmed S. S. Abdullah

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

ISSN: 2303-4521

Digital Object Identifier DOI: 10.21533/pen

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License