Face Recognition Based on Stacked Convolutional Autoencoder and Sparse Representation

Face recognition is one of the most challenging topics in the field of machine vision and pattern recognition, and has a wide range of applications. The face features play an important role in the classification, while the features extracted by traditional methods are simple and elementary. To solve this problem, a stacked convolutional autoencoder (SCAE) based on deep learning theory is used to extract deeper features. The output of the encoder can be taken to design a feature dictionary. Meanwhile sparse representation is a general classification algorithm which has shown the good performance in the field of object recognition. In this paper a framework based on stacked convolutional autoencoder and sparse representation is proposed. Experiments, carried out with the LFW face database, have shown that the proposed framework can extract more deep and abstract features by multi-level cascade, and has high recognition speed and high accuracy.