Reconstruction and recognition of face and digit images using autoencoders

This paper presents techniques for image reconstruction and recognition using autoencoders. Experiments are conducted to compare the performances of three types of autoencoder neural networks based on their efficiency of reconstruction and recognition. Reconstruction error and recognition rate are determined in all the three cases using the same architecture configuration and training algorithm. The results obtained with autoencoders are also compared with those obtained using principal component analysis method. Instead of whole images, image patches are used for training, and this leads to much simpler autoencoder architectures and reduced training time.