Face pose recognition is one of the challenging areas in computer vision. Cross-pose change causes the change in the information of face appearance. The maximization of intrasubject correlation helps to widen the intersubject differences which helps further in achieving pose invariance. In this paper, for cross pose recognition, the authors propose to maximize the cross pose correlation by using the logically concatenated cross binary pattern (LC-CBP) descriptor and two dimensional canonical correlation analysis (2DCCA). The LC-CBP descriptor extracts the local texture details of face images with low computation complexity and the 2DCCA explicitly searches for the maximization of the correlated features to retain most informative content. Joint feature consideration via 2DCCA helps in setting up a better correspondence between a discrete set of nonfrontal pose and the frontal pose of the same subject. Experimental results demonstrate the two dimensional canonical correlation LC-CBP descriptor along with intensity values improve the correlation.
A latent variable model for two-dimensional canonical correlation analysis and the variational inference
