Analysis and evaluation of regression-based methods for facial pose classification

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.