Autoencoder versus PCA in face recognition

The paper presents the comparative analysis of the computer systems for face recognition. Autoencoder, the typical representative of deep learning is compared with the classical PCA transformation. Both, autoencoder and PCA serve as the tools for feature generation and selection. However, the important difference is the nonlinearity and multilayer structure applied in autoencoder. Final task of recognition is done by the support vector machine or softmax circuit. The numerical results performed on the multiclass base of faces have shown superiority of autoencoding principle, especially when the number of recognized classes is very high.