Face recognition based on cnn 2d-3d reconstruction using shape and texture vectors combined

This study proposes a face recognition model using a combination of shape and texture vectors that are used to produce new face images on 2D-3D reconstruction images. The reconstruction process to produce 3D face images is carried out using the convolutional neural network (CNN) method on 2D face images. Merging shapes and textures vector is used to produce correlation points on new face images that have similarities to the initial image used. Principal Component Analysis (PCA) is used as a feature extraction method, and for the classification method, we use the Mahalanobis method. The results of the tests can produce a better recognition rate compared to face recognition testing using 2D images. © 2019 Institute of Advanced Engineering and Science.