Exploiting Wavelet Transform, Principal Component Analysis, Support Vector Machine, and K-Nearest Neighbors for Partial Face Recognition

  • Mustafa H. Mohammed Alhabib Department of Communications and Computer Engineering, Cihan University-Erbil, Kurdistan Region, Iraq.
  • Mustafa Zuhaer Nayef Al-Dabagh Department of Computer Science, Knowledge University, Kurdistan Region, Iraq
  • Firas H. AL-Mukhtar Department of Information Technology, Catholic University in Erbil, Kurdistan Region, Iraq
  • Hussein Ibrahim Hussein Department of Computer and Communication Engineering, Universiti Malaysia Perlis, Malaysia
Keywords: K-nearest neighborhood, Partial face recognition, Principal component analysis, Support vector machine, Wavelet transform

Abstract

Facial analysis has evolved to be a process of considerable importance due to its consequence on the safety and security, either individually or generally on the society level, especially in personal identification. The paper in hand applies facial identification on a facial image dataset by examining partial facial images before allocating a set of distinctive characteristics to them. Extracting the desired features from the input image is achieved by means of wavelet transform. Principal component analysis is used for feature selection, which specifies several aspects in the input image; these features are fed to two stages of classification using a support vector machine and K-nearest neighborhood to classify the face. The images used to test the strength of the suggested method are taken from the well-known (Yale) database. Test results showed the eligibility of the system when it comes to identify images and assign the correct face and name.

Downloads

Download data is not yet available.

References

P. Hu, H. Ning, T. Qiu, Y. Xu, X. Luo and A. Sangaiah. “Unified face identification and resolution scheme using cloud computing in internet of things”. Future Generation Computer Systems, vol. 81, pp. 582-592, 2018.

A. Tolba, A. El-Baz and A. El-Harby. “Face recognition: A literature review”. International Journal of Signal Processing, vol. 2, no. 2, pp. 88-92, 2005.

L. Paul and A. Sumam. “Face recognition using principal component analysis method”. International Journal of Advanced Research in Computer Engineering and Technology, vol. 1, no. 9, pp. 135-139, 2012.

S. Kak, F. Mustafa and P. Valente. “Discrete wavelet transform with eigenface to enhance face recognition rate”. Academic Journal of Nawroz University, vol. 7, no. 4, pp. 10-13, 2018.

C. Bhimanwar, R. Biradar, N. Bhole and M. Rane. “Face identification”. International Journal of Engineering Science and Computing, vol. 7, no. 5, pp. 11923-11928, 2017.

M. Mukhedkar and S. Powalkar. “Fast Face Recognition Based on Wavelet Transform on PCA”. International Conference on Energy Systems and Applications (ICESA 2015), India, 2015.

G. Cavallaro, M. Riedel, M. Richerzhagen, J. Benediktsson and A. Plaza. “On understanding big data impacts in remotely sensed image classification using support vector machine methods”. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 8, no. 10, pp. 4634-4646, 2015.

J. Wang, J. Zheng, S. Zhang, J. He, X. Liang and S. Feng. “A Face Recognition System Based on Local Binary Patterns and Support Vector Machine for Home Security Service Robot”. 9th International Symposium on Computational Intelligence and Design. IEEE, 2016.

Y. Cha, K. You and W. Choi. “Vision-based detection of loosened bolts using the Hough transform and support vector machines”. Automation in Construction, vol. 71, no. 2, pp. 181-188.

I. Kavakiotis, O. Tsave, A. Salifoglou, N. Maglaveras, I. Vlahavas and I. Chouvarda. “Machine learning and data mining methods in diabetes research”. Computational and Structural Biotechnology Journal, vol. 15, pp. 104-116, 2017.

T. Patel and B. Shah. “A Survey on Facial Feature Extraction Techniques for Automatic Face Annotation”. International Conference on Innovative Mechanisms for Industry Applications (ICIMIA). IEEE, 2017.

Y. Song, J. Huang, D. Zhou, H. Zha1 and C. Giles. “IKNN: Informative K-Nearest Neighbor Pattern Classification”. Springer- Verlag, Berlin Heidelberg, 2007.

K. Weinberger and L. Saul. “Distance metric learning for large margin nearest neighbor classification”. Journal of Machine Learning Research, vol. 10, pp. 207-244, 2009.

M. Z. N. Al-Dabagh, M. H. M. Alhabib and F. H. AL-Mukhtar. “Face recognition system based on kernel discriminant analysis, k-nearest neighbor and support vector machine”. International Journal of Research and Engineering, vol. 5, no. 3, pp. 335-338, 2018.

N. Pedrajas, J. Castillo and G. García. “A Proposal for Local k Values for k-Nearest Neighbor Rule”. IEEE Transactions on Neural Networks and Learning Systems, vol. 28, no. 2, pp. 470-475, 2015.

G. Hemalatha and C. Sumathi. “Preprocessing Techniques of Facial Image with Median and Gabor Filters”. International Conference on Information Communication and Embedded System (ICICES). IEEE, 2016.

M. Sukassini and T. Velmurugan. “Noise Removal using Morphology and Median Filter Methods in Mammogram Images”. The 3rd International Conference on Small and Medium Business, 2016.

S. Perumal and T. Velmurugan. “Preprocessing by contrast enhancement techniques for medical images”. International Journal of Pure and Applied Mathematics, vol. 118, no. 18, pp. 3681-3688, 2018.

K. Ghazali, M. Mansor, M. Mustafa and A. Hussain. “Feature Extraction Technique using Discrete Wavelet Transform for Image Classification”. The 5th Student Conference on Research and Development SCOReD. IEEE, 2007.

J. Choi, J. Choi, M. Ha, T. Trinh, T. Yoon and H. Byun. “Towards a generalized toxicity prediction model for oxide nanomaterials using integrated data from different sources”. Scientific Reports, vol. 8, pp. 6110, 2018.

Published
2019-08-20
How to Cite
Alhabib, M., Al-Dabagh, M., AL-Mukhtar, F., & Hussein, H. (2019). Exploiting Wavelet Transform, Principal Component Analysis, Support Vector Machine, and K-Nearest Neighbors for Partial Face Recognition. Cihan University-Erbil Scientific Journal, 3(2), 80-84. https://doi.org/10.24086/cuesj.v3n2y2019.pp80-84
Section
Research Article