How to cite this paper
AlFawwaz, B., AL-Shatnawi, A., Al-Saqqar, F., Nusir, M & Yaseen, H. (2022). Face recognition system based on the multi-resolution singular value decomposition fusion technique.International Journal of Data and Network Science, 6(4), 1249-1260.
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AL-Shatnawi, A., Al-Saqqar, F., El-Bashir, M. and Nusir, M., (2021). Face Recognition Model based on the Laplacian Pyramid Fusion Technique. International Journal of Advances in Soft Computing & Its Applications, 13(1).
Annu, & Sharma, A. (2016). A Review Study on Face Recognition Procedure and System. International Journal of Tech-nical Research (IJTR), 5(2).
Balola, O.A. and Shaout, A., 2016. Hybrid Arabic Handwritten Character Recognition Using PCA and ANFIS. In International Arab Conference on Information Technology (ACIT’2016).
Bansal, A., Mehta, K., & Arora, S. (2012, January). Face recognition using PCA and LDA algorithm. In 2012 second in-ternational conference on Advanced Computing & Communication Technologies (pp. 251-254). IEEE.
De Carrera, P., & Marques, I. (2010). Face recognition algorithms. Master's thesis in Computer Science, Universidad Euskal Herriko.
Ding, H. (2016). Combining 2D Facial Texture and 3D Face Morphology for Estimating People's Soft Biometrics and Recognizing Facial Expressions. PhD thesis. Université de Lyon.
El-Bashir, M. S., AL-Shatnawi, A. M., Al-Saqqar, F., & Nusir, M. I. (2021). Face Recognition Model Based on Covari-ance Intersection Fusion for Interactive devices. World of Computer Science & Information Technology Journal, 11(2).
Gardner, M. W., & Dorling, S. R. (1998). Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences. Atmospheric environment, 32(14-15), 2627-2636.
Guo, Q., Chen, S., Leung, H. and Liu, S., (2010). Covariance intersection based image fusion technique with application to pansharpening in remote sensing. Information Sciences, 180(18), 3434-3443.
Haghighat, M. B. A., Aghagolzadeh, A., & Seyedarabi, H. (2011). Multi-focus image fusion for visual sensor networks in DCT domain. Computers & Electrical Engineering, 37(5), 789-797.
Huang, J., Yuen, P.C., Lai, J.H. and Li, C.H., (2004). Face recognition using local and global features. EURASIP Journal on Advances in Signal Processing, 2004(4), pp.1-12.
Jafri, R., & Arabnia, H. R. (2009). A survey of face recognition techniques. Journal of information processing systems, 5(2), 41-68.
Jagalingam, P., & Hegde, A. V. (2014). Pixel level image fusion—a review on various techniques. In 3rd World Conf. on Applied Sciences, Engineering and Technology.
Kaur, M. (2012). K-nearest neighbor classification approach for face and fingerprint at feature level fusion. International Journal of Computer Applications, 60(14), 13-17.
Kittler, J., Hatef, M., Duin, R. P., & Matas, J. (1998). On combining classifiers. IEEE transactions on pattern analysis and machine intelligence, 20(3), 226-239.
Le, T. H. (2011). Applying artificial neural networks for face recognition. Advances in Artificial Neural Systems, 2011.
Liu, L., Zhao, L., Long, Y., Kuang, G., & Fieguth, P. (2012). Extended local binary patterns for texture classification. Im-age and Vision Computing, 30(2), 86-99.
Mirza, A. M., Hussain, M., Almuzaini, H., Muhammad, G., Aboalsamh, H., & Bebis, G. (2013, July). Gender recognition using fusion of local and global facial features. In International Symposium on Visual Computing (pp. 493-502). Springer, Berlin, Heidelberg.
Naidu, V. P. S. (2011). Novel image fusion techniques multi-resolution singular value decomposition. Defense Science Journal, 61(5), 479.
Nguyen, H. (2014). Contributions to facial feature extraction for face recognition, PhD thesis. Université de Grenoble.
Nusir, M (2018), Face Recognition using Local Binary Pattern and Principle Component Analysis, Master's thesis in Computer Science, Al al-Bayt University, Jordan,
Ojala, T., Pietikäinen, M., & Harwood, D. (1996). A comparative study of texture measures with classification based on featured distributions. Pattern recognition, 29(1), 51-59.
Pearson, K. (1901). LIII. On lines and planes of closest fit to systems of points in space. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 2(11), 559-572.
Ramya Priya, K. (2014). Illumination Based Robust Face Recognition System. International Journal of Modern Trends in Engineering and Science, 3(2).
Singh, M., Singh, R., & Ross, A. (2019). A comprehensive overview of biometric fusion. Information Fusion, 52, 187-205.
Štruc, V., Gros, J. Z., Dobrišek, S., & Pavešic, N. (2013). Exploiting representation plurality for robust and efficient face recognition. In Proceedings of the 22nd International Electrotechnical and Computer Science Conference (ERK’13) (pp. 121-124).
Taigman, Y., Wolf, L., & Hassner, T. (2009, September). Multiple One-Shots for Utilizing Class Label Information. In BMVC (Vol. 2, pp. 1-12).
Tan, X., & Triggs, B. (2007, October). Fusing Gabor and LBP feature sets for kernel-based face recognition. In Interna-tional workshop on analysis and modeling of faces and gestures (pp. 235-249). Springer, Berlin, Heidelberg.
Tharwat, A. (2016). Principal component analysis-a tutorial. International Journal of Applied Pattern Recognition, 3(3), 197-240.
Tran, C. K., Lee, T. F., Chang, L., & Chao, P. J. (2014, June). Face description with local binary patterns and local ternary patterns: improving face recognition performance using similarity feature-based selection and classification algo-rithm. In 2014 International Symposium on Computer, Consumer and Control (pp. 520-524). IEEE.
Viola, P., & Jones, M. (2001, December). Rapid object detection using a boosted cascade of simple features. In Proceed-ings of the 2001 IEEE computer society conference on computer vision and pattern recognition. CVPR 2001 (Vol. 1, pp. I-I). IEEE.
Viola, P., & Jones, M. J. (2004). Robust real-time face detection. International journal of computer vision, 57(2), 137-154.
Wang, H., Hu, J., & Deng, W. (2017). Face feature extraction: a complete review. IEEE Access, 6, 6001-6039.
Wang, Y. Q. (2014). An analysis of the Viola-Jones face detection algorithm. Image Processing On Line, 4, 128-148.
Wolf, L., Hassner, T., & Taigman, Y. (2009, September). Similarity scores based on background samples. In Asian Con-ference on Computer Vision (pp. 88-97). Springer, Berlin, Heidelberg.
Zhang, X., Mahoor, M. H., & Mavadati, S. M. (2015). Facial expression recognition using lp-norm MKL multiclass-SVM. Machine Vision and Applications, 26(4), 467-483.
Zhao, W., Chellappa, R., Phillips, P. J., & Rosenfeld, A. (2003). Face recognition: A literature survey. ACM computing surveys (CSUR), 35(4), 399- 458.
Al-Saqqar, F., AL-Shatnawi, A., Al-Diabat, M., & Aloun, M. (2019). Handwritten Arabic text recognition using principal component analysis and support vector machines. International journal of advanced computer science and applica-tions, 10(12), 1-6.
AL-Shatnawi, A., Al-Saqqar, F., El-Bashir, M. and Nusir, M., (2021). Face Recognition Model based on the Laplacian Pyramid Fusion Technique. International Journal of Advances in Soft Computing & Its Applications, 13(1).
Annu, & Sharma, A. (2016). A Review Study on Face Recognition Procedure and System. International Journal of Tech-nical Research (IJTR), 5(2).
Balola, O.A. and Shaout, A., 2016. Hybrid Arabic Handwritten Character Recognition Using PCA and ANFIS. In International Arab Conference on Information Technology (ACIT’2016).
Bansal, A., Mehta, K., & Arora, S. (2012, January). Face recognition using PCA and LDA algorithm. In 2012 second in-ternational conference on Advanced Computing & Communication Technologies (pp. 251-254). IEEE.
De Carrera, P., & Marques, I. (2010). Face recognition algorithms. Master's thesis in Computer Science, Universidad Euskal Herriko.
Ding, H. (2016). Combining 2D Facial Texture and 3D Face Morphology for Estimating People's Soft Biometrics and Recognizing Facial Expressions. PhD thesis. Université de Lyon.
El-Bashir, M. S., AL-Shatnawi, A. M., Al-Saqqar, F., & Nusir, M. I. (2021). Face Recognition Model Based on Covari-ance Intersection Fusion for Interactive devices. World of Computer Science & Information Technology Journal, 11(2).
Gardner, M. W., & Dorling, S. R. (1998). Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences. Atmospheric environment, 32(14-15), 2627-2636.
Guo, Q., Chen, S., Leung, H. and Liu, S., (2010). Covariance intersection based image fusion technique with application to pansharpening in remote sensing. Information Sciences, 180(18), 3434-3443.
Haghighat, M. B. A., Aghagolzadeh, A., & Seyedarabi, H. (2011). Multi-focus image fusion for visual sensor networks in DCT domain. Computers & Electrical Engineering, 37(5), 789-797.
Huang, J., Yuen, P.C., Lai, J.H. and Li, C.H., (2004). Face recognition using local and global features. EURASIP Journal on Advances in Signal Processing, 2004(4), pp.1-12.
Jafri, R., & Arabnia, H. R. (2009). A survey of face recognition techniques. Journal of information processing systems, 5(2), 41-68.
Jagalingam, P., & Hegde, A. V. (2014). Pixel level image fusion—a review on various techniques. In 3rd World Conf. on Applied Sciences, Engineering and Technology.
Kaur, M. (2012). K-nearest neighbor classification approach for face and fingerprint at feature level fusion. International Journal of Computer Applications, 60(14), 13-17.
Kittler, J., Hatef, M., Duin, R. P., & Matas, J. (1998). On combining classifiers. IEEE transactions on pattern analysis and machine intelligence, 20(3), 226-239.
Le, T. H. (2011). Applying artificial neural networks for face recognition. Advances in Artificial Neural Systems, 2011.
Liu, L., Zhao, L., Long, Y., Kuang, G., & Fieguth, P. (2012). Extended local binary patterns for texture classification. Im-age and Vision Computing, 30(2), 86-99.
Mirza, A. M., Hussain, M., Almuzaini, H., Muhammad, G., Aboalsamh, H., & Bebis, G. (2013, July). Gender recognition using fusion of local and global facial features. In International Symposium on Visual Computing (pp. 493-502). Springer, Berlin, Heidelberg.
Naidu, V. P. S. (2011). Novel image fusion techniques multi-resolution singular value decomposition. Defense Science Journal, 61(5), 479.
Nguyen, H. (2014). Contributions to facial feature extraction for face recognition, PhD thesis. Université de Grenoble.
Nusir, M (2018), Face Recognition using Local Binary Pattern and Principle Component Analysis, Master's thesis in Computer Science, Al al-Bayt University, Jordan,
Ojala, T., Pietikäinen, M., & Harwood, D. (1996). A comparative study of texture measures with classification based on featured distributions. Pattern recognition, 29(1), 51-59.
Pearson, K. (1901). LIII. On lines and planes of closest fit to systems of points in space. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 2(11), 559-572.
Ramya Priya, K. (2014). Illumination Based Robust Face Recognition System. International Journal of Modern Trends in Engineering and Science, 3(2).
Singh, M., Singh, R., & Ross, A. (2019). A comprehensive overview of biometric fusion. Information Fusion, 52, 187-205.
Štruc, V., Gros, J. Z., Dobrišek, S., & Pavešic, N. (2013). Exploiting representation plurality for robust and efficient face recognition. In Proceedings of the 22nd International Electrotechnical and Computer Science Conference (ERK’13) (pp. 121-124).
Taigman, Y., Wolf, L., & Hassner, T. (2009, September). Multiple One-Shots for Utilizing Class Label Information. In BMVC (Vol. 2, pp. 1-12).
Tan, X., & Triggs, B. (2007, October). Fusing Gabor and LBP feature sets for kernel-based face recognition. In Interna-tional workshop on analysis and modeling of faces and gestures (pp. 235-249). Springer, Berlin, Heidelberg.
Tharwat, A. (2016). Principal component analysis-a tutorial. International Journal of Applied Pattern Recognition, 3(3), 197-240.
Tran, C. K., Lee, T. F., Chang, L., & Chao, P. J. (2014, June). Face description with local binary patterns and local ternary patterns: improving face recognition performance using similarity feature-based selection and classification algo-rithm. In 2014 International Symposium on Computer, Consumer and Control (pp. 520-524). IEEE.
Viola, P., & Jones, M. (2001, December). Rapid object detection using a boosted cascade of simple features. In Proceed-ings of the 2001 IEEE computer society conference on computer vision and pattern recognition. CVPR 2001 (Vol. 1, pp. I-I). IEEE.
Viola, P., & Jones, M. J. (2004). Robust real-time face detection. International journal of computer vision, 57(2), 137-154.
Wang, H., Hu, J., & Deng, W. (2017). Face feature extraction: a complete review. IEEE Access, 6, 6001-6039.
Wang, Y. Q. (2014). An analysis of the Viola-Jones face detection algorithm. Image Processing On Line, 4, 128-148.
Wolf, L., Hassner, T., & Taigman, Y. (2009, September). Similarity scores based on background samples. In Asian Con-ference on Computer Vision (pp. 88-97). Springer, Berlin, Heidelberg.
Zhang, X., Mahoor, M. H., & Mavadati, S. M. (2015). Facial expression recognition using lp-norm MKL multiclass-SVM. Machine Vision and Applications, 26(4), 467-483.
Zhao, W., Chellappa, R., Phillips, P. J., & Rosenfeld, A. (2003). Face recognition: A literature survey. ACM computing surveys (CSUR), 35(4), 399- 458.