Using Neural Networks with Differential Evolution Learning for Face Recognition

In this paper, we present an innovative method that combines two-dimensional texture and three-dimensional (3D) images surface feature vectors. Next, we use Gabor wavelets extracting local features at different scales and orientations by two-dimensional facial images. Next, we combine the texture with the three-dimensional (3D) images surface feature vectors based on principal component analysis (PCA) to obtain feature vectors from grey and facial surface images. We also propose a differential evolution (DE) algorithm for face recognition based on multilayer neural networks as an identification model. In ours experimental results demonstrate for the recognition different face poses and facial expressions method was efficiency.