Periocular Recognition in the Wild: Implementation of RGB-OCLBCP Dual-Stream CNN

Abstract

Periocular recognition remains challenging for deployments in the unconstrained environments. Therefore, this paper proposes an RGB-OCLBCP dual-stream convolutional neural network, which accepts an RGB ocular image and a colour-based texture descriptor, namely Orthogonal Combination-Local Binary Coded Pattern (OCLBCP) for periocular recognition in the wild. The proposed network aggregates the RGB image and the OCLBCP descriptor by using two distinct late-fusion layers. We demonstrate that the proposed network benefits from the RGB image and thee OCLBCP descriptor can gain better recognition performance. A new database, namely an Ethnic-ocular database of periocular in the wild, is introduced and shared for benchmarking. In addition, three publicly accessible databases, namely AR, CASIA-iris distance and UBIPr, have been used to evaluate the proposed network. When compared against several competing networks on these databases, the proposed network achieved better performances in both recognition and verification tasks.