Performance Evaluation of Different Feature Extractors and Classifiers for Recognition of Human Faces with Low Resolution Images

Year 2015, Volume: 3 Issue: 2, 72 - 77, 01.04.2015

Soodeh Nikan , Majid Ahmadi

https://doi.org/10.18201/ijisae.28949

Abstract

Face recognition is an effective biometric identification technique used in many applications such as law enforcement, document validation and video surveillance. In this paper the effect of low resolution images which are captured in real world applications, on the performance of different feature extraction techniques combined with a variety of classification approaches is evaluated.  Gabor features and its combination with local phase quantization histogram (GLPQH) are dimensionality reduced by principal component analysis (PCA), linear discriminant analysis (LDA), locally sensitive discriminant analysis (LSDA) and neighbourhood preserving embedding (NPE) to extract discriminant image characteristics and the class label is attributed using the extreme learning machine (ELM), sparse classifier (SC), fuzzy nearest neighbour (FNN) or regularized discriminant classifier (RDC). ORL and AR databases are utilized and the results show that ELM and RDC have better performance and stability against resolution reduction, especially on Gabor-PCA and Gabor-LDA techniques. Among the interpolation approaches that we employed to enhance the image resolution, nearest neighbour outperforms other methods.

Keywords

Face recognition, Feature Extraction, Classification, Interpolation, Dimensionality Reduction

References

• G. B. Huang, H. Zhou, X. Ding and R. Zhang (2012). Extreme Learning Machine for Regression and Multiclass Classification. IEEE Transactions on Systems, Man and Cybernetics. Vol.45. Pages.513-529.
• S. M. Metev and V. P. Veiko (2011). Practical Image And Video Processing Using MATLAB. New Jersey: John Wiley & Sons. Hoboken 1st ed.
• S. Nikan and M. Ahmadi (2014). Study of The Effectiveness of Various Feature Extractors for Human Face Recognition for Low Resolution Images. Proc. AISE’05. Pages. 1-6.
• S. Nikan and M. Ahmadi (2014). Effectiveness of Various Classification Techniques on Human Face Recognition. Proc. HPCS’14. In Press.
• The AT&T Laboratories Cambridge Website. [Online]. Available:
• http://www.cl.cam.ac.uk/research/dtg/attarchive/facedataba se.html.
• A. Martinez and R. Benavente (1998). The AR Face Database. CVC Technical Report. Vol. 24. [Online]. Available:
• <http://www2.ece.ohio-state.edu/~aliex/ARdatabase.html>.