# Periocular Recognition in the Wild with Orthogonal Combination of Local Binary Coded Pattern in Dual-stream Convolutional Neural Network

## TL;DR

This paper introduces a dual-stream CNN with a novel color-based texture descriptor, OC-LBCP, for improved periocular recognition in unconstrained environments, and provides a new dataset for benchmarking.

## Contribution

It proposes a multilayer fusion dual-stream CNN incorporating OC-LBCP and introduces a new dataset for periocular recognition in the wild.

## Key findings

- The proposed method outperforms existing approaches on benchmark datasets.
- Late-fusion layers improve recognition accuracy.
- The new Ethnic-ocular dataset provides a valuable resource for future research.

## Abstract

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 (dual-stream) convolutional neural network where each network accepts RGB data and a novel colour-based texture descriptor, namely Orthogonal Combination-Local Binary Coded Pattern (OC-LBCP) for periocular recognition in the wild. Specifically, two distinct late-fusion layers are introduced in the dual-stream network to aggregate the RGB data and OC-LBCP. Thus, the network beneficial from this new feature of the late-fusion layers for accuracy performance gain. We also introduce and share a new dataset for periocular in the wild, namely Ethnic-ocular 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.