# Periocular Recognition in the Wild: Implementation of RGB-OCLBCP Dual-Stream CNN
## 1 Introduction
### Motivation and Contributions  
In the early days of periocular recognition, the problems were mostly concerned about what was the best way to handle periocular in the presence of illuminations, pose alignments, and occlusions [5; 6]. Many periocular databases were built using carefully controlled images for each of these issues. UBIPr [12], CASIA-iris database [29], and MICHE database [30] are the most comprehensive efforts in this direction and created in a well-controlled environment.  
Presently, the challenges of periocular recognition concern about images that having large variations due to in the wild environments, such as ageing, appearances, cameras location, level of illuminations, occlusions, pose alignments, and others [18; 31]. In addition, many existing databases [12; 13; 29; 30] and research communities [18; 23; 27] still yet to prepare for periocular recognition in the wild challenge. Especially, the appearances of periocular with cosmetic products, and plastic surgery can affect the recognition performance negatively.  
This paper offers a solution for periocular recognition in the wild by investigating the fusion of RGB periocular images and a novel texture descriptor, i.e., OCLBCP, by means of a dual-stream CNN. OCLBCP exploits the colour information in the periocular texture to better represent the periocular features for recognition in the wild. The two networks share the parameters and a late fusion takes place at the last _conv_ layer before _fc_ layer.  
For validation of the proposed network, a new database is introduced, namely Ethnic-ocular, by collecting the periocular region images in the wild setup. The databased includes five ethnic groups: _African_, _Asian_, _Latin American_, _Middle Eastern_, and _White_. The database is created in such a way that each ethnic group has a unique shape of periocular and skin texture of periocular regions [32]. Therefore, the database avoids unbalanced selection, as there are differences in the configuration of oculars among different ethnicities.  
Hence, the contributions of this paper are as follows:  
* To study complementarity between CNN and input features, we investigate and analyse the combination of RGB image and a novel texture descriptor, namely OCLBCP for periocular recognition in the wild.  
* Two distinct late-fusion layers are introduced in the proposed CNN. The role of the late-fusion layers is to aggregate the RGB image and OCLBCP descriptor. Hence, the proposed two-stream CNN is beneficial from these new features of the late-fusion layers to deliver better accuracy performance.
* A new periocular in the wild database, namely Ethnic-ocular, is created and shared in [33]. The images were collected across highly uncontrolled subject-camera distances, appearances, resolutions, locations, levels of illumination, and so on. The database includes training and testing schemes for performance analysis and evaluation.  
The paper is organised as follows: Section 2 describes the structure of the proposed colour-based Orthogonal Combination-Local Binary Coded Pattern (OCLBCP) texture descriptor. The proposed network with fusion algorithm is presented in Section 3 and the detailed database information is presented in Section 4. Section 5 discusses the experimental results and analysis. A conclusion is summarised in Section 6.