Face Recognition using a Newly Developed Linear Subspace Learning Method

Authors

  • Ali Khalili Mobarakeh Department of Mechanical Engineering and Fluids Mechanic, University of Malaga, Spain.
  • Juan Antonio Cabrera Carrillo Department of Mechanical Engineering and Fluids Mechanic, University of Malaga, Spain.
  • Juan Jesus Castillo Agui-lar Department of Mechanical Engineering and Fluids Mechanic, University of Malaga, Spain.
  • Shadi Mahmoodi Khaniabadi Department of Mechanical Engineering and Fluids Mechanic, University of Malaga, Spain.
  • Saba Nazari Department of Mechanical Engineering and Fluids Mechanic, University of Malaga, Spain.

Keywords:

Face Recognition, Biometrics, Learning Method, KNN,

Abstract

Face recognition is considered a specific physiological biometric in order to identify an individual according to the physical features of the human face. Much research has been conducted in such areas, but still more accurate processes are required for biometric facial recognition. This article presents a novel linear subspace learning method for face recognition which not only can take advantage of principle components analysis (PCA) as a successful feature extraction, but also can apply nearest local centroid mean vector (LMKNCN) as an effective classifier to improve the classification performance. The main goal of this particular scheme is to handle two common existing issues in recognition techniques: named sensitivity to the training sample size and negative effects of outliers. Moreover, to illustrate the performance of proposed developed PCA, we compare it with the latest dimensionality reduction techniques such as traditional PCA and KPCA on publicly available face dataset. Experimental results illustrate that our newly developed method has significantly achieved better performance over the same face database compared with the former KNN-based algorithms.

References

Mahri, N., S.A.S. Suandi, and B.A. Rosdi, "Finger Vein Recognition Algorithm Using Phase Only Correlation". Emerging Techniques and Challenges for Hand-Based Biometrics (ETCHB), 2010 International Workshop on., pp. 1-6, 2010.

Wayman, J.L., "Biometric Verification/Identification/Authentication/Recognition", The Terminology. Encyclopedia of Biometrics, pp. 263-268, 2015.

Eui Chul Lee, H.J., Daeyeoul Kim, "New Finger Biometric Method Using Near Infrared Imaging". Sensors, vol. 11, no. 3, pp. 2319-2333, 2011.

Hadjikhani, N., et al., "Improving emotional face perception in autism with diuretic bumetanide: a proof-of-concept behavioral and functional brain imaging pilot study". Autism, vol. 19, no. 2, pp. 149-157, 2015.

Bhattacharyya, D., "Biometric Authentication: A Review". Science and Technology, vol. 2, pp. 13-28, 2015.

Rinaldi, A., "Biometrics' new identity—measuring more physical and biological traits". EMBO reports, vol. 17, no. 1, pp. 22-26, 2016.

Schouten, B. and B. Jacobs, "Biometrics and their use in e-passports". Image and Vision Computing, vol. 27, no. 3, pp. 305-312, 2015.

Damavandinejadmonfared, S., et al., "Finger vein recognition using PCA-based methods". World Academy of Science, Engineering and Technology, vol. 66, pp. 2012, 2012.

Bowyer, K.W., BIOMETRICS RESEARCH. 2016.

Ko, K.W., et al., "Development of Human Identification System Based on Simple Finger-Vein Pattern-Matching Method for Embedded Environments". International Journal of Security and Its Applications, vol. 9, no. 5, pp. 297-306, 2015.

Mohod, P.S. and K.C. Jondhale, "Face Recognition Using PCA". International Journal of Artificial Intelligence and Knowledge Discovery, vol. 1, no. 1, pp. 25-28, 2011.

Kim, W., S. Suh, and J.-J. Han, "Face Liveness Detection from a Single Image via Diffusion Speed Model". Image Processing, IEEE Transactions on, vol. 24, no. 8, pp. 2456-2465, 2015.

Singh, N.A., M.B. Kumar, and M.C. Bala, "Face Recognition System based on SURF and LDA Technique". 2016.

Bishop, C.M., Pattern Recognition. Machine Learning, 2006.

Bro, R. and A.K. Smilde, "Principal component analysis". Analytical Methods, vol. 6, no. 9, pp. 2812-2831, 2014.

Gou, J., et al., "A Local Mean-Based k-Nearest Centroid Neighbor Classifier". The Computer Journal, pp. 1-14, 2012.

Hechenbichler, K., "Weighted k-Nearest-Neighbor Techniques and Ordinal Classification". 2004.

Huang, S.-Y., Y.-R. Yeh, and S. Eguchi, "Robust Kernel Principal Component Analysis". Neural Computation, vol. 21, no. 11, pp. 3179-3213, 2009.

Mobarakeh, A., et al. "Applying Weighted K-nearest centroid neighbor as classifier to improve the finger vein recognition performance". in Control System, Computing and Engineering (ICCSCE), 2012 IEEE International Conference on, 2012.

Delac, K., M. Grgic, and S. Grgic, "Independent comparative study of PCA, ICA, and LDA on the FERET data set". International Journal of Imaging Systems and Technology, vol. 15, no. 5, pp. 252-260, 2005.

Fix , E. and J.L.Hodges, "Discriminatory Analysis, Nonparametric Discrimination". Vol. 57, pp. 238-247, 1951.

Agrawal, S. and P. Khatri. "Facial expression detection techniques: based on Viola and Jones algorithm and principal component analysis". in Advanced Computing & Communication Technologies(ACCT), 2015 Fifth International Conference on, 2015.

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Published

2017-04-01

How to Cite

Mobarakeh, A. K., Cabrera Carrillo, J. A., Castillo Agui-lar, J. J., Mahmoodi Khaniabadi, S., & Nazari, S. (2017). Face Recognition using a Newly Developed Linear Subspace Learning Method. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9(1-5), 141–144. Retrieved from https://jtec.utem.edu.my/jtec/article/view/1852

Issue

Vol. 9 No. 1-5: Smart City Technology in Communication and Computer Engineering III

Section

Articles

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