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Robust face recognition algorithm based on linear operators discrete wavelet transformation and simple linear regression

Published: 01 October 2018 Publication History

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Abstract

Face recognition system performance would sharply decrease if there were noticeable issues in the face images conditions such as light variation, contrast, and brightness issues that can deeply affect the system performance directly. The process of face analysis comes here to put the face image environment in a spot of light to enable the interested researchers to find out the factors that have vital effects on these systems. In this paper, we are producing a hybrid method that based on discrete wavelet transformation DWT output and linear edge detection operators such as Sobel, Prewitt and Roberts output as a solution to cover some of these related image condition issues. For feature extraction, a new method based on simple linear regression slope with -SLP name-that proved the ability to find features in critical regions of the face, and eigenface based on principal components analysis PCA used with linear edge detection operators for comparison, studying the interrelation among them, and investigation the effects on the performance of the proposed system. A segmentation used to handle the details of a face image by dividing dataset images to equaled size blocks. Modified Artificial neural network MANN used for classification and all results obtained evaluated. The proposed method examined and evaluated with different face datasets using modified MANN classifier. The experimental results were displaying the superiority of the proposed algorithm over the algorithms that used the state-of-art techniques.

References

[1]
Turk, M. a., & Pentland, A. P. (1991). Face Recognition Using Eigenfaces. Journal of Cognitive Neuroscience
[2]
Bartlett, M. S. (2001). Independent Component Representations for Face Recognition. Face Image Analysis by Unsupervised Learning, 3967.
[3]
Belhumeur, P. N., Hespanha, J. P., & Krieg man, D. J. (1997). Eigenfaces vs. fisher face: Recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7), 711720.
[4]
Lu, J., Member, S., Platinoids, K. N., & Venetsanopoulos, A. N. (2003). Face Recognition Using Kernel Direct Discriminant Analysis Algorithms. Analysis, 14(1), 117126.
[5]
Lawrence, S., Giles, C. L., Ah Chung Tsoi, & Back, A. D. (1997). Face recognition: a convolutional neural-network approach. IEEE Transactions on Neural Networks, 8(1), 98113.
[6]
Karatzoglou, A., Meyer, D., & Hornik, K. (2006). Support Vector Algorithm in R. Journal of Statistical Software, 15(9), 128.
[7]
Pratt, W. K. (2001). Processing Digital Image Processing. Image Rochester NY (Vol.5).
[8]
Vincent, O. R., & Folorunso, O. (2009). A Descriptive Algorithm for Sobel Image Edge Detection. Proceedings of Informing Science & IT Education Conference (InSITE) 2009, 1--11.
[9]
Maini, R., & Aggarwal, H. (2009). Study and comparison of various image edge detection techniques. International Journal of Image Processing, 3(1), 1--11. https://doi.org/http://www.doaj.org/doaj?func=openurl&genre=article&issn=19852304&date=2009&volume=3&issue=1&spage=1
[10]
Zhang, H., Zhu, Q., Fan, C., & Deng, D. (2013). Image quality assessment based on Prewitt magnitude. AEU - International Journal of Electronics and Communications, 67(9), 799--803.
[11]
Pratt, W. K. (2001). Processing Digital Image Processing. Image Rochester NY (Vol. 5).
[12]
Chaple, G. N., Daruwala, R. D., & Gofane, M. S. (2015). Comparisons of Robert, Prewitt, Sobel operator based edge detection methods for real time use on FPGA. Technologies for Sustainable Development (ICTSD), 2015 International Conference on, (1), 4--7.
[13]
Symposium, I., & Processing, I. (2017). Iww 2017 9.
[14]
http://vis-www.cs.umass.edu/lfw/
[15]
http://www.pitt.edu/emotion/ck-spread.htm
[16]
http://www.jdl.ac.cn/peal/
[17]
http://www.kasrl.org/jaffe.html
[18]
BioID-Face-Database @ www.bioid.com. (n.d.). Retrieved from https://www.bioid.com/About/BioID-Face-Database
[19]
Revisited, S., & Sorting, P. (2004). Introduction to Algorithms Part 1 : Divide and Conquer Sorting and Searching. Sort.
[20]
Edelsbrunner, H., Assistant, T., & Gu, Z. (2008). Design and Analysis.
[21]
Barford, L. A., Fazio, R. S., & Smith, D. R. (1992). An introduction to wavelets. Hewlett-Packard Labs, Bristol, UK, Tech. Rep. HPL-92-124, 2, 129.
[22]
Yamamoto, A., & T. L. Lee, D. (1994). Wavelet Analysis: Theory and Applications. Hewlett-Packard Journal, (December), 44--52.
[23]
Discrete, T. H. E., & Transform, W. (n.d.). No Title, 1--15.
[24]
Alorf, A. A. (2016). Performance evaluation of the PCA versus improved PCA (IPCA) in image compression, and in face detection and recognition. 2016 Future Technologies Conference (FTC), (December), 537--546.
[25]
Kang, J., Lin, X., & Yang, G. (2015). Research of Multi-Scale PCA Algorithm for Face Recognition.
[26]
Kumar, D. S. D., & Rao, P. V. (2015). Analysis and Design of Principal Component Analysis and Hidden Markov Model for Face Recognition. Procedia Materials Science, 10(Cnt 2014), 616--625.
[27]
Liu, K., & Moon, S. (2016). Robust dual-stage face recognition method using PCA and high-dimensional-LBP. 2016 IEEE International Conference on Information and Automation (ICIA), (August), 1828--1831.
[28]
Alazzawi, Osman, Bayat, face recognition based on multi features extractors" IEEE, (2017).
[29]
Alazzwi, Osman, face recognition based on gradient operators Symposium, I., & Processing, I. (2017). Iww
[30]
Alazzawi, A., Ucan, O. N., & Bayat, O. (2018). Evaluation of Face Recognition Techniques Using Edge Detection Operators, Discrete Wavelate Transformation and New Feature Extraction Method Based on Linear Regression Slope.
[31]
Aljawarneh, S. A. and Vangipuram, R. 2018. GARUDA: Gaussian dissimilarity measure for feature representation and anomaly detection in Internet of things. The Journal of Supercomputing, 1--38.
[32]
Aljawarneh, S. A., Vangipuram, R., Puligadda, V. K., and Vinjamuri, J. 2017. G-SPAMINE: An approach to discover temporal association patterns and trends in internet of things. Future Generation Computer Systems, 74, 430--443.
[33]
Shadi A. Aljawarneh, Muneer Bani Yassein, and We'am Adel Talafha. 2017. A resource-efficient encryption algorithm for multimedia big data. Multimedia Tools Appl. 76, 21 (November 2017), 22703--22724.
[34]
Shadi A. Aljawarneh, Ali Alawneh, and Reem Jaradat. 2017. Cloud security engineering. Future Gener. Comput. Syst. 74, C (September 2017), 385--392.

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  • (2019)VLAProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/33512613:3(1-19)Online publication date: 9-Sep-2019

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DATA '18: Proceedings of the First International Conference on Data Science, E-learning and Information Systems
October 2018
274 pages
ISBN:9781450365369
DOI:10.1145/3279996
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 01 October 2018

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Author Tags

  1. MANN
  2. PCA
  3. SLP
  4. face analysis
  5. face recognition
  6. neural network

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  • (2019)VLAProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/33512613:3(1-19)Online publication date: 9-Sep-2019

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