One shot learning approach for cross spectrum periocular verification

The use of face mask during the COVID-19 pandemic has increased the popularity of the periocular biometrics in surveillance applications. Despite of the rapid advancements in this area, matching images over cross spectrum is still a challenging problem. Reason may be two-fold 1) variations in image illumination 2) small size of available data sets and/or class imbalance problem. This paper proposes Siamese architecture based convolutional neural networks which works on the concept of one-shot classification. In one shot classification, network requires a single training example from each class to train the complete model which may lead to reduce the need of large dataset as well as doesn't matter whether the dataset is imbalance. The proposed architectures comprise of identical subnetworks with shared weights whose performance is assessed on three publicly available databases namely IMP, UTIRIS and PolyU with four different loss functions namely Binary cross entropy loss, Hinge loss, contrastive loss and Triplet loss. In order to mitigate the inherent illumination variations of cross spectrum images CLAHE was used to preprocess images. Extensive experiments show that the proposed Siamese CNN model with triplet loss function outperforms the states of the art periocular verification methods for cross, mono and multi spectral periocular image matching.