Face Recognition based on CNN 2D-3D Reconstruction using Shape and Texture Vectors Combining

Edy Winarno (Universitas Stikubank Semarang)
Imam Husni Al Amin (Universitas Stikubank Semarang)
Sri Hartati (Universitas Gadjah Mada)
Prajanto Wahyu Adi (Universitas Dian Nuswantoro)

Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
Vol 8, No 2: June 2020

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, 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.