Periocular Recognition in the Wild with Orthogonal Combination of Local Binary Coded Pattern in Dualstream Convolutional Neural Network In spite of the advancements made in the periocular recognition, the dataset and periocular recognition in the wild remains a challenge. In this paper, we propose a multilayer fusion approach by means of a pair of shared parameters (dualstream) convolutional neural network where each network accepts RGB data and a novel colourbased texture descriptor, namely Orthogonal CombinationLocal Binary Coded Pattern (OCLBCP) for periocular recognition in the wild. Specifically, two distinct latefusion layers are introduced in the dualstream network to aggregate the RGB data and OCLBCP. Thus, the network beneficial from this new feature of the latefusion layers for accuracy performance gain. We also introduce and share a new dataset for periocular in the wild, namely Ethnicocular dataset for benchmarking. The proposed network has also been assessed on one publicly available dataset, namely UBIPr. The proposed network outperforms several competing approaches on these datasets.

Comparative Convolutional Neural Network for Younger Face Identification We consider the problem of determining whether a pair of face images can be distinguishable in terms of age and if so, which is the younger of the two. We also determine the degree of distinguishability in which age differences are categorized into large, medium, small and tiny. We propose a comparative convolutional neural network combining two parallel deep architectures. Based on the two deep learnt face features, we introduce a comparative layer to represent their mutual relationships, followed by a concatenatation implementation. Softmax is adopted to complete the classification task. To demonstrate our approach, we construct a very large dataset consisting of over 1.7 million face image pairs with youngold labels.