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Considering challenges such as occlusions due to face coverings, ocular biometrics have risen as a potential remedy. Visible light ocular modalities use features extracted from in and around the eyes from ‘selfie’ captures for personal identification. A less‐visited ocular neighbourhood is the eyebrows region. Previous works on eyebrows have been mostly using subject‐specific verification protocols where identities overlap between the training and test sets and do not directly address the issue of doppelgangers and twins. Here, the evaluation of five deep learning models, lightCNN, ResNet, DenseNet, MobileNetV2, and SqueezeNet, for eyebrow‐based user authentication in a subject independent environment across different data sets, lighting conditions, resolutions, and facial expressions is done. Also a challenging simulated identical twins scenario in our training and testing data sets is presented. Our results show a 4.2% equal error rate (EER) and 0.993 area under the curve (AUC) on a subset of the FACES data set (154 subjects) and 6.8% EER, 0.978 AUC on a subset of the VISOB data set (350 subjects). Despite achieving promising accuracy during short‐term evaluations, the proposed methods showed lower accuracy when matching against long‐term data or when facing data captured under varying illumination and facial expressions.

Occlusion due to eyeglasses is one of the main challenges affecting the face and general ocular recognition, including eyebrow matching. In this study, the authors propose a convolutional neural network (CNN)-based method for (a) eyeglasses detection and segmentation to mitigate its impact on personal recognition in mobile devices and (b) use the shape of the glasses as a soft token of identity (something that one has). They evaluated the efficacy of the proposed eyeglasses segmentation on eyebrow matching and eyeglasses-based user authentication. To this front, various texture and deep features were evaluated. Using the publicly available large-scale visible ocular biometric dataset, they show that the proposed methods provide (a) eyeglasses detection and segmentation accuracies of 100 and 97% using CNNs, (b) a 2.51% reduction in eyebrow matching error by removing eyeglass occlusions and (c) eyeglasses matching with a 96.6% accuracy.

Automated gender prediction has drawn significant interest in numerous applications such as surveillance, human–computer interaction, anonymous customised advertisement system, image retrieval system, and biometrics. In the context of smartphone devices, gender information has been used to enhance the accuracy of the integrated biometric authentication and mobile healthcare system. Here, the authors thoroughly investigate gender prediction from ocular images acquired using front-facing cameras of smartphones. This is a new problem as previous research in this area has not explored RGB ocular images captured by smartphones. The authors used deep learning for the task. Specifically, pre-trained and custom convolutional neural network architectures have been implemented for gender prediction. Multi-classifier fusion has been used to improve the prediction accuracy. Further, evaluation of off-the-self-texture descriptors and study of human ability in gender prediction has been conducted for comparative analysis.

Biometric-based person recognition poses a challenging problem because of large variability in biometric sample quality encountered during testing and a restricted number of enrollment samples for training. Solutions in the form of adaptive biometrics have been introduced to address this issue. These adaptive biometric systems aim to adapt enrolled templates to variations in samples observed during operations. However, despite numerous advantages, few commercial vendors have adopted auto-update procedures in their products. This is due in part to the limited understanding and limitations associated with existing adaptation schemes. In view of that the topic of adaptive biometrics has not been systematically investigated, this study works towards filling this gap by surveying the topic from a growing body of the recent literature and by providing a coherent view (critical analysis) of the limitations of the existing systems. In addition, the authors have also identified novel research directions and proposed a novel framework. The overall aim is to advance the state-of-the-art and improve the quality of discourse in this field.