PROJECT TITLE :

Head Pose Estimation From a 2D Face Image Using 3D Face Morphing With Depth Parameters

ABSTRACT:

This paper presents estimation of head cause angles from one 2D face image using a 3D face model morphed from a reference face model. A reference model refers to a 3D face of a person of the identical ethnicity and gender because the query subject. The proposed scheme minimizes the disparity between the two sets of distinguished facial features on the question face image and therefore the corresponding points on the 3D face model to estimate the pinnacle create angles. The 3D face model used is morphed from a reference model to be additional specific to the query face in terms of the depth error at the feature points. The morphing process produces a 3D face model additional specific to the question image when multiple 2D face images of the query subject are available for coaching. The proposed morphing process is computationally economical since the depth of a 3D face model is adjusted by a scalar depth parameter at feature points. Optimal depth parameters are found by minimizing the disparity between the 2D features of the query face image and the corresponding features on the morphed 3D model projected onto 2D space. The proposed head create estimation technique was evaluated on two benchmarking databases: 1) the USF Human-ID database for depth estimation and 2) the Pointing’04 database for head cause estimation. Experiment results demonstrate that head cause estimation errors in nodding and shaking angles are as low as seven.93° and 4.65° on average for one 2D input face image.


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