Adaptive Deep Supervised Autoencoder Based Image Reconstruction for Face Recognition

Based on a special type of denoising autoencoder (DAE) and image reconstruction, we present a novel supervised deep learning framework for face recognition (FR). Unlike existing deep autoencoder which is unsupervised face recognition method, the proposed method takes class label information from training samples into account in the deep learning procedure and can automatically discover the underlying nonlinear manifold structures. Specifically, we define an Adaptive Deep Supervised Network Template (ADSNT) with the supervised autoencoder which is trained to extract characteristic features from corrupted/clean facial images and reconstruct the corresponding similar facial images. The reconstruction is realized by a so-called “bottleneck” neural network that learns to map face images into a low-dimensional vector and reconstruct the respective corresponding face images from the mapping vectors. Having trained the ADSNT, a new face image can then be recognized by comparing its reconstruction image with individual gallery images, respectively. Extensive experiments on three databases including AR, PubFig, and Extended Yale B demonstrate that the proposed method can significantly improve the accuracy of face recognition under enormous illumination, pose change, and a fraction of occlusion.