A Novel Implementation Algorithm for Calculation of Common Vectors

Year 2016, Volume: 17 Issue: 2, 251 - 262, 14.07.2016

Mehmet Koç Atalay Barkana

https://doi.org/10.18038/btda.68101

Abstract

Common vector approach (CVA), discriminative common vector approach (DCVA) and linear regression classification (LRC) are subspace methods used in pattern recognition. Up to now, there were two well-known algorithms to calculate the common vectors: (i) by using the Gram-Schmidt orthogonalization process, (ii) by using the within-class covariance matrices. The purpose of this paper is to introduce a new implementation algorithm for the derivation of the common vectors using the linear regression idea. The derivation of the discriminative common vectors through LRC is also included in this paper. Two numerical examples are given to clarify the proposed derivations. An experimental work is given in AR face database to compare the recognition performances of CVA, DCVA, and LRC. Additionally, the three implementation algorithms of common vector are compared in terms of processing time efficiency.

Keywords

Common vector, Discriminative common vector, Linear regression classification, Subspace methods, Face recognition

A NOVEL IMPLEMENTATION ALGORITHM FOR CALCULATION OF

Abstract

References

• Shakhnarovich G, Moghaddam B. Face recognition in subspaces In: Li SZ, Jain AK, editors. Handbook of Face Recognition. Springer-Verlag; 2004. pp. 141-168.
• Gulmezoglu MB, Dzhafarov V, Barkana A. The common vector approach and its relation to principal component analysis. IEEE T Speech and Audi P 2001; 9(6): 655-662.
• Cevikalp H, Neamtu M, Wilkes M, Barkana A. Discriminative common vectors for face recognition. IEEE T Pattern Anal 2005; 27(1): 4-13.
• Naseem I, Togneri R, Bennamoun M. Linear regression for face recognition. IEEE T Pattern Anal 2010; 32(11): 2106-2112.
• Koc M, Barkana A, Gerek ON. A fast method for the implementation of common vector approach. Inform Sciences 2010; 180(11): 4084-4098.
• Gulmezoglu MB, Keskin M, Dzhafarov V, Barkana A. A novel approach to isolated word recognition. IEEE T Speech and Audi P 1999; 7(6): 620-628.
• Lu GF, Zou J, Wang Y. Incremental learning of discriminant common vectors for feature extraction. Appl Math Comput 2012; 218(22): 11269-11278.
• Koc M, Barkana A. An implementation of discriminative common vector approach using matrices. In: The Seventh International Multi-Conference on Computing in the Global Information Technology(ICCGI2012); 24-29 June 2012; Venice, Italy. pp. 260-263.
• Cevikalp H, Neamtu M, Barkana A. The kernel common vector method: a novel nonlinear subspace classifier for pattern recognition. IEEE T Syst Man Cy B 2007; 37(4): 937-951.
• Diaz-Chito K, Ferri FJ, Diaz-Villanueva W. Image recognition through incremental discriminative common vectors. Advenced Concepts for Intelligent Vision Systems 2010; 6475: 304-311.
• Huang SM, Yang JF. Linear discriminant regression classification for face recognition. IEEE Signal Proc Let 2013; 20(1): 91-94. [12] Huang SM, Yang JF. Improved principal component regression for face recognition under illumination variations. IEEE Signal Proc Let 2012; 19(4): 179-182.
• Naseem I, Togneri R, Bennamoun M. Robust regression for face recognition. Pattern Recogn 2012; 45(1): 104-118.
• Koc M, Barkana A. Application of linear regression classification to low dimensional datasets. Neurocomputing 2014; 131: 331-335.
• Martinez A, Benavente Y. The AR face database, CVC Technical Report 24, 1994.
• Koc M, Barkana A. Discriminative common vector approach based feature selection in face recognition, Computers & Electrical Engineering, 40(8):37-50, 2014.