We present a face recognition technique based on a special type of convolutional neural network that is trained to extract characteristic features from face images and reconstruct the corresponding reference face images which are chosen beforehand for each individual to recognize. The reconstruction is realized by a so-called "bottle-neck" neural network that learns to project face images into a low-dimensional vector space and to reconstruct the respective reference images from the projected vectors. In contrast to methods based on the Principal Component Analysis (PCA), the Linear Discriminant Analysis (LDA) etc., the projection is non-linear and depends on the choice of the reference images. Moreover, local and global processing are closely interconnected and the respective parameters are conjointly learnt. Having trained the neural network, new face images can then be classified by comparing the respective projected vectors. We experimentally show that the choice of the reference images influences the final recognition performance and that this method outperforms linear projection methods in terms of precision and robustness.