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.

Towards Accurate and Lightweight Masked Face Recognition: An Experimental Evaluation

“…It was shown that in all cases an improvement between 3 and 5 percent was achieved when using both eyes instead of only one. Thus, most of the state-of-the art methods use the bi-ocular information, some of them analyzing left and right eyes separately and then combining the results [36], while others use both eyes within a single image [14].…”

“…On the other hand, some works propose feature fusion approach which combines handcrafted features (e.g. LBP and HOG) with features extracted using pretrained CNN models [18], [36]. Another hybrid model is introduced in [1] for ocular smartphone authentication (Selfie Biometrics).…”

“…Unlike other ocular traits such as iris, retinal and conjunctival vasculature, the acquisition of the periocular biometrics does not require high user cooperation and close capture distance [31], which is in particular useful for COVID-19 pandemic. Recent methods for periocular recognition based on deep learning models have shown promising results even for in-the-wild images [13], [16], [36]. Nevertheless, since periocular biometrics encloses only the immediate vicinity of eyes, i.e., a sub-region of a face, it captures relatively less information compared with that of the face.…”

Impact on Biometric Identification Systems of COVID-19

“…Similarly, periocular recognition applies to the recognition process around the eye. According to [4], a dualstream convolutional neural network model can be applied to the periocular region to obtain more color information, which means that the network can accept RGB images. In addition, a new descriptor, the orthogonal combinatoriallocal binary coding model, is proposed.…”

Identification of crystal symmetry from noisy diffraction patterns by a shape analysis and deep learning

“…The idea of sharing parameters imposes prior knowledge that the inputs to each substream (S R , S G , and S B ) are processed concurrently by the network, which substantially reduces the number of parameters in the MSDN; there is some speedup of the optimization process. In addition, this method warrants different layers in each stream be functionally equivalent after training, which is known to be beneficial to preventing extrapolation biases; that is, the network can adapt better to outof-domain examples than networks without shared parameters 35,36 . In addition, the MSDN utilizes the design concept of DenseNet 37 , in which all layers are densely connected (Fig.…”