3D face construction from single 2D images using DEO model

Abstract

In recent years, considerable attention has been paid to 3D face data in many face image processing applications. Detailed 3D Face making is developing technology with multiple real-time applications. This work aims to create an exact 3D Face model with facial emotions designed based on the principle of the Face Vertex Land marking and Wulcheir distance. Convolution Neural Network (DCNN) is deployed to extract relevant facial features and those features are used for further analysis. The 3D Face models are constructed efficiently. The proposed model is a concoction of CoarseNet and FineNet through which a 3D coarse face from a bilinear face model with face landmark alignment is created. It is followed by the local corrective field which tends to refine the 3D rough face with consistent photometric constraint. This work follows the various aspects of 3D face modeling techniques: Deep Learning, Epiploic Geometry, and the One-shot learning (DEO) method. The proposed DEO Model has been evaluated using the FER2013 dataset of face images with six basic emotions via performance metrics like accuracy, precision, sensitivity, specificity, and time. The proposed model outperforms other existing methods with promising and state-of-art results. The accuracy obtained through the proposed work shows higher accuracy (more than 90%), which has been demonstrated using real-world models