Abstract
Convolutional Neural Networks have provided state-ofthe-art results in several computer vision problems. However, due to a large number of parameters in CNNs, they
require a large number of training samples which is a limiting factor for small sample size problems. To address this
limitation, we propose SSF-CNN which focuses on learning
the “structure” and “strength” of filters. The structure of
the filter is initialized using a dictionary based filter learning algorithm and the strength of the filter is learned using
the small sample training data. The architecture provides
the flexibility of training with both small and large training databases, and yields good accuracies even with small
size training data. The effectiveness of the algorithm is first
demonstrated on MNIST, CIFAR10, and NORB databases,
with varying number of training samples. The results show
that SSF-CNN significantly reduces the number of parameters required for training while providing high accuracies
on the test databases. On small sample size problems such
as newborn face recognition and Omniglot, it yields stateof-the-art results. Specifically, on the IIITD Newborn Face
Database, the results demonstrate improvement in rank-1
identification accuracy by at least 10%.