  The Convolutional Neural Networks (CNN) have become very popular recently due
to its outstanding performance in various computer vision applications. It is
also used over widely studied face recognition problem. However, the existing
layers of CNN are unable to cope with the problem of hard examples which
generally produce lower class scores. Thus, the existing methods become biased
towards the easy examples. In this paper, we resolve this problem by
incorporating a Parametric Sigmoid Norm (PSN) layer just before the final
fully-connected layer. We propose a PSNet CNN model by using the PSN layer. The
PSN layer facilitates high gradient flow for harder examples as compared to
easy examples. Thus, it forces the network to learn the visual characteristics
of hard examples. We conduct the face recognition experiments to test the
performance of PSN layer. The suitability of the PSN layer with different loss
functions is also experimented. The widely used Labeled Faces in the Wild (LFW)
and YouTube Faces (YTF) datasets are used in the experiments. The experimental
results confirm the relevance of the proposed PSN layer.
