2D Face image depth ordering using adaptive Hillcrest-Valley classification and Otsu

Abstract

Reconstruction of a 3D face from a single 2D face image can be substantially useful in many areas such as security, forensic, 3D animation, and motion capture. Most 3D face reconstruction techniques require multiple 2D face images taken at different views in order to estimate the depth of each face component which is one of the most crucial parameters in 3D face reconstruction. We propose a new method that is capable of estimating the depth order of a 2D face image in various illumination conditions. The proposed method uses Hillcrest-Valley classification with adaptive mean filter and Otsu threshold value selection to estimate the depth order of an image. The proposed method was evaluated on the YaleB face database consists of 10 individual frontal faces for the total of 576 face images and the experimental results reveal that the proposed method is robust and it can estimate the depth order of a frontal face up to as many as 50 layers regardless of lighting condition.