Learning Sparse Face Features: Application to Face Verification

We present a low resolution face recognition technique based on a Convolutional Neural Network approach. The network is trained to reconstruct a reference per subject image. In classical feature-based approaches, a first stage of features extraction is followed by a classification to perform the recognition. In classical Convolutional Neural Network approaches, features extraction stages are stacked (interlaced with pooling layers) with classical neural layers on top to form the complete architecture of the network. This paper addresses two questions: 1. Does a pretraining of the filters in an unsupervised manner improve the recognition rate compared to the one with filters learned in a purely supervised scheme? 2. Is there an advantage of pretraining more than one feature extraction stage? We show particularly that a refinement of the filters during the supervised training improves the results.