Face Recognition Use Local Image Dataset and Correlation Technique

Authors

  • Dana Faiq Abd Department of Information Technology, College of Science and Technology, University of Human Development, Sulaimani City, Iraq

DOI:

https://doi.org/10.21928/uhdjst.v5n2y2021.pp26-31

Abstract

Face recognition is an extreme topic in security field which identifies humans through physiological or behavioral biometric characteristics. Face recognition can also identify the human almost in a precise detection; one of the primary problems in face recognition is the accurate recognition rate. Local datasets use for implementing this research rather than using public datasets. Midian filter uses to remove noise and identify errors, also obtains a good accuracy rate without modifying image quality. In addition, filter processing applies to modify and progress images and the discrete wavelet transforms algorithm uses as feature extraction. Many steps are applied in this approach such as image acquisition, converting images into gray scale, cropping the image, and then passing to the feature extraction. In order to get the final decision about the indicated face, some required steps are used in the comparison. The results show the accuracy of 91% of the recognition rate through the human face.

References

R. Malathi and R. R. R. Jeberson. “An integrated approach of physical biometric authentication system” Procedia Computer Science, vol. 85, pp. 820-826, 2016.

F. Battaglia, G. Iannizzotto and L. Lo Bello. “A person authentication system based on RFID tags and a cascade of face recognition algorithms”. IEEE Transactions on Circuits and Systems for Video Technology, vol. 27, no. 8, pp. 1676-1690, 2017.

A. James, I. Fedorova, T. Ibrayev and D. Kudithipudi. “HTM spatial pooler with memristor crossbar circuits for sparse biometric recognition”. IEEE Transactions on Biomedical Circuits and Systems, vol. 11, no. 3, pp. 640-651, 2017.

A. Punnappurath, A. Rajagopalan, S. Taheri, R. Chellappa and G. Seetharaman. “Face recognition across non-uniform motion blur, illumination, and pose”. IEEE Transactions on Image Processing, vol. 24, no. 7, pp. 2067-2082, 2015.

L. Zhang, P. Dou and I. Kakadiaris. “Patch-based face recognition using a hierarchical multi-label matcher”. Image and Vision Computing, vol. 73, pp. 28-39, 2018.

Z. A. Kakarash, D. F. Abd, M. Al-Ani, G. Abubakr and K. M. Omar. “Biometric Iris recognition approach based on filtering techniques”. Journal of the University of Garmian, vol. 6, no. 2, p. 34243, 2019.

L. Best-Rowden and A. Jain. “Longitudinal study of automatic face recognition”. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40, no. 1, pp. 148-162, 2018.

I. Masi, F. Chang, J. Choi, S. Harel, J. Kim, K. Kim, J. Leksut, S. Rawls, Y. Wu, T. Hassner, W. AbdAlmageed, G. Medioni, L. Morency, P. Natarajan and R. Nevatia. “Learning pose-aware models for pose-invariant face recognition in the wild”. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 41, no. 2, pp. 1-1, 2018.

M. Sharif, S. Bhagavatula, L. Bauer and M. Reiter. “Accessorize to a Crime”. Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security-CCS’16, United States, 2016.

A. Aboshosha, K. A. El Dahshan, E. A. Karam and E. A. Ebeid. “Score level fusion for fingerprint, Iris and face biometrics”. International Journal of Computer Applications, vol. 111, no. 4, pp. 47-55, 2015.

Y. Xu, Z. Zhang, G. Lu and J. Yang. “Approximately symmetrical face images for image preprocessing in face recognition and sparse representation based classification”. Pattern Recognition, vol. 54, pp. 68-82, 2016.

S. Karahan, M. Kilinc Yildirum, K. Kirtac, F. Rende, G. Butun and H. Ekenel. How Image Degradations Affect Deep CNN-based Face Recognition?” 2016 International Conference of the Biometrics Special Interest Group, Darmstadt, Germany, 2016.

G. Gao, J. Yang, X. Y. Jing, F. Shen, W. Yang and D. Yue. “Learning robust and discriminative low-rank rep-resentations for face recognition with occlusion”. Pattern Recognition, vol. 66, pp. 129- 143, 2017.

C. Y. Wu and J. J. Ding. “Occluded face recognition using low-rank regression with gen- eralized gradient direction”. Pattern Recognition, vol. 80, pp. 256-268, 2018.

P. Jing, Y. Su, Z. Li, J. Liu and L. Nie. “Low-rank regularized tensor discriminant representation for image set classification”. Signal Processing, vol. 156, pp. 62-70, 2019.

S. B. Chen, C. H. Q. Ding and B. Luo. “Extended linear regression for undersampled face recognition”. Journal of Visual Communication and Image Representation, vol. 25, no. 7, pp. 1800-1809, 2014.

A. Dehghan, O. Oreifej and M. Shah. “Complex event recognition using con-strained low-rank representation”. Image and Vision Computing, vol. 42, pp. 13-21, 2015.

C. H. Zheng, Y. F. Hou and J. Zhang. “Improved sparse representation with low-rank representation for robust face recognition”. Neurocomputing, vol. 198, pp. 114-124, 2016.

E. Hjelmås. “Biometric Systems: A Face Recognition Approach”. Department of Informatics University of Oslo, Oslo, Norway.

R. Chellappa, C. L. Wilson, and S. Sirohey. “Human and machine recognition of faces: A survey”. Proceedings of the IEEE, vol. 83, no. 5, pp. 405-741, 1995.

P. N. Belhumeur, J. P. Hespanha and D. J. Kriegman. “Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection”. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, p.7, 1997.

Z. A. Kakarash, S. H. T. Karim and Mohammadi, M. “Fall detection using neural network based on internet of things streaming data”. UHD Journal of Science and Technology, vol. 4, no. 2, pp. 91-98, 2020.

R. V. Chawngsangpuii and K. S. Yumnam. “Different approaches to face recognition”. International Journal of Engineering Research and Technology, vol. 4, no. 9, pp. 71-75, 2015.

C. Kyong, K. W. Bowyer and S. Sarkar. “Comparison and combination of ear and face images in appearance-based biometrics”. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 9, pp. 1160-1165, 2013.

O. H. Ahmed, J. Lu, Q. Xu and M. S. Al-Ani. “Face recognition based rank reduction SVD approach”. The ISC International Journal of Information Security, vol. 11, no. 3, p. 6, 2019.

A. S. Tolba, A. H. El-Baz and A. A. El-Harby. “Face recognition: A literature review”. World Academy of Science, Engineering and Technology, vol. 2, pp. 7-21, 2008.

P. D. Wadkar and M. Wankhade. “Face recognition using discrete wavelet transforms”. International Journal of Advanced Engineering Technology, vol. 3, pp. 239-242, 2012.

S. K. Dandapat and S. Meher. “Performance improvement for face recognition using PCA and two-dimensional PCA”. IEEE International Conference on Computer Communication and Informatics, vol. 2013, pp. 1-5, 2013.

G. V. Sagar, S. Y. Barker, K. B. Raja, K. S. Babu and K. R. Venugopal. “Convolution Based Face Recognition Using DWT and Feature Vector Compression”. Institute of Electrical and Electronics Engineers Conference Paper, United States, 2015.

D. Leonidas. “Emerging Trends in Image Processing, Computer Vision and Pattern Recognition. Elsevier, Huntsville, AL, USA, pp. 183-199, 2015.

L. Cornelius. “Multidimensional Systems: Signal Processing and Modeling Techniques”. Elsevier, Los Angeles, USA, 1995.

Published

2021-08-05

Issue

Section

Articles

Most read articles by the same author(s)