Automatic Frontal Face Reconstruction Approach for Pose Invariant Face Recognition

Abstract

Handling pose variations for face recognition system is a challenging task. The recognition rate is drastically decreasing with the images captured in uncontrolled environment having pose variations in yaw, pitch and roll angles. When the face image with frontal pose it is proved that the recognition system performs well. In this research an attempt is made to reconstruct frontal pose face images from non-frontal face images to improve the face recognition accuracy. By estimating the change in pose with respect to yaw, pitch and roll angles based on the landmark points best viewed side of the pose is identified. Using tilting, stretching and mirroring operation to the best viewed side, frontal pose is obtained. This approach is database independent, training free and no need to generate 3D model and not using any fitting approach, which is a complex task and handle any combination of roll, yaw, pitch angle up to ± 22.5 degrees only from the 2D landmark points. Experiments were conducted on FERET, HP, LFW, PUB-FIG data bases and the experimental result proves that our approach can handle the uncontrolled faces with arbitrary poses Experimental results on various controlled and uncontrolled poses proved the effectiveness of the proposed method.