Authors: Ajay Jaiswal; Nitin Kumar; R.K. Agrawal
Addresses: S.S. College of Business Studies, University of Delhi, New Delhi, India ' Department of Computer Science and Engineering, National Institute of Technology, Uttarakhand, India ' School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi, India
Abstract: Facial pose classification is one of the important steps in some pose invariant face recognition methods. Regression has been used for facial pose classification. In this paper, facial pose classification approaches using different types of regression are compared in terms of average classification accuracy and computation time. We also analyse the time complexity of regression-based approaches for pose classification. Performance of these approaches is also compared with other popular approaches in terms of classification accuracy. Experimental results on two publicly available face databases (PIE and FERET) show that the performance of regression-based approaches is comparable and generally outperform other approaches. Among regression-based methods, local linear regression with overlap outperforms other methods. In terms of computation time, global linear regression and nonlinear regression are comparable and better than others. We also analysed the performance of regression-based approaches after adding Gaussian noise with zero mean in test images and found that global linear regression and nonlinear regression-based approaches perform better than other.
Keywords: global linear regression; GLR; local linear regression; LLR; pose classification; facial pose; face recognition.
International Journal of Applied Pattern Recognition, 2015 Vol.2 No.1, pp.24 - 45
Received: 22 Mar 2014
Accepted: 21 Sep 2014
Published online: 21 Apr 2015 *