Scarce face recognition via two-layer collaborative representation

The recent significant progress in face recognition is mainly achieved using learning-based (LE) techniques via an exhaustive training involving a huge number of face samples. However, in many applications, the number of face images available for training may be very limited. This makes LE techniques impractical for learning discriminative features and models. Thus, limited number of face samples (i.e. scarce data) degrades the recognition performance of most existing methods. To overcome this problem, the authors propose a novel approach based on two-layer collaborative representation to exploit the abundance of samples in some classes to enrich the scarce data in other classes. The first-layer collaborative representation uses the abundance of samples to construct representations for the scarce data. Then, a new face sample is recognised by computing residuals with the second-layer collaborative representation. Extensive experiments on four benchmark face databases demonstrate the effectiveness of their proposed approach which compares favourably against state-of-the-art methods.