# Periocular Recognition in the Wild: Implementation of RGB-OCLBCP Dual-Stream CNN
## 1 Introduction
### Related Works  
The early study on periocular biometrics presented in [5] shows promising results in human recognition. The authors adopted several handcrafted descriptors such as Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP) and Scale-Invariant Feature Transform (SIFT) as periocular feature representation, followed by the score fusion for classification. [PERSON] et al. [14] and [PERSON] et al. [15] also introduced a similar approach, which convolves periocular features extracted from HOG or LBP feature matrix with Gabor filters and followed by score fusion. There are several research articles focused on combinations of texture descriptors with fusion algorithm for periocular representation and recognition [16; 17; 18; 19; 20]. All these approaches are mainly focused on amalgamation of various handcrafted texture descriptors and followed by learning machines for decent performances in periocular recognition. However, these approaches are less robust to \"in the wild\" variations such as resolutions, levels of illumination, poses, and occlusions due to inadequacy and inflexibility of handcrafted texture descriptors in representing periocular features. Therefore, the periocular recognition in the wild remains a challenge. In recent years, CNNs have gained escalating attention in image classification [21; 22]. CNNs can be used to extract image texture features from different layers while handcrafted texture descriptor are only limited to low-level features, which is equivalent to the first convolutional (_conv_) layer features of CNNs. Apart from _conv_ layers at different level, the features can be extracted from max pooling (_maxpool_) and fully-connected (_fc_) layers of CNNs. Several researchers have employed CNNs for periocular recognition. For instance, [PERSON] et al. [23] proposed two CNNs (for left and right oculars), namely DeepIrisNet, which extracts comprehensive information to boost recognition  
Figure 1: Samples of periocular regions. We demonstrate sample images of the periocular region that including eyebrows. The images are collected from The Korea Times [10] and Kitchen Decor [11]. performance. Other studies, e.g., by [PERSON] et al. [24] and [PERSON] et al. [25], have demonstrated enhanced CNN frameworks for periocular recognition where the prior knowledge is exploited to discard unnecessary information. [PERSON] et al. [24] suggested removal/separation of the iris and sclera from the periocular regions, while [PERSON] et al. [25] identified the critical regions (only included eyebrow and eye region) that can extract more discriminative information to improve periocular recognition. However, these networks were found to underperform when there are misalignments of periocular images, images missing the eyebrows and images missing ocular. The relevant works that deal with non-ideal ocular are those by [PERSON] et al. [26] and [PERSON] et al. [27]. [PERSON] et al. [26] fused iris and periocular modalities through a weighted concatenation. The network achieved significant results when compared to other CNNs. Similarly, [PERSON] et al. [27] invented a new multimodal CNN, namely multi-fusion CNN, where the iris, face and fingerprint features are fused at _fc_ layer. A fusion layer is designed to fuse different levels of _fc_ layers as multi-feature representations with the sole RGB image. However, these works leveraged several biometrics where all of them may not always be available such as occluded face with mouth covered or iris from a distance. Furthermore, the use of multiple biometrics modality may jeopardise the usability of the system such as fingerprint and iris need cooperation from the users. In the previous work of CNN that consumes face texture descriptor, [PERSON] et al. [28] demonstrated the use of colour-based LBP descriptor as input to CNN rather than raw RGB face image for emotional recognition. The authors showed that colour-based texture descriptor is useful to train their network in the wild environment.