Independent analysis of feature based face recognition algorithms under varying poses

Feature based face recognition algorithms are computationally efficient compared to model based approaches. These algorithms have proved themselves for face identification under variations in poses. However, the literature lacks with direct and detailed investigation of these algorithms in completely equal working conditions. This motivates us to carry out an independent performance analysis of well known feature based face identification algorithms for different poses with mug-shot face database situation. The analysis focuses on variations in performance of feature based algorithms in terms of identification rates due to variation in poses. The analysis is carried out in face identification scenario using large amount of images from the standard face databases such as AT&T, Georgian Face database and Head Pose Image database. We analysed state-of-the art feature based algorithms such as PCA, log Gabor, DCT and FPLBP and found that, log Gabor outperforms for larger degree of pose variation with an average identification rate 82.47% with three training images for Head Pose Image database.