Face recognition is a popular and well-studied area with wide applications in our society. However, racial bias had been proven to be inherent in most State Of The Art (SOTA) face recognition systems. Many investigative studies on face recognition algorithms have reported higher false positive rates of African subjects cohorts than the other cohorts. Lack of large-scale African face image databases in public domain is one of the main restrictions in studying the racial bias problem of face recognition. To this end, we collect a face image database namely CASIA-Face-Africa which contains 38,546 images of 1,183 African subjects. Multi-spectral cameras are utilized to capture the face images under various illumination settings. Demographic attributes and facial expressions of the subjects are also carefully recorded. For landmark detection, each face image in the database is manually labeled with 68 facial keypoints. A group of evaluation protocols are constructed according to different applications, tasks, partitions and scenarios. The performances of SOTA face recognition algorithms without re-training are reported as baselines. The proposed database along with its face landmark annotations, evaluation protocols and preliminary results form a good benchmark to study the essential aspects of face biometrics for African subjects, especially face image preprocessing, face feature analysis and matching, facial expression recognition, sex/age estimation, ethnic classification, face image generation, etc. The database can be downloaded from our website.

Bias and fairness of biometric algorithms have been key topics of research in recent years, mainly due to the societal, legal and ethical implications of potentially unfair decisions made by automated decision-making models. A considerable amount of work has been done on this topic across different biometric modalities, aiming at better understanding the main sources of algorithmic bias or devising mitigation measures. In this work, we contribute to these efforts and present the first study investigating bias and fairness of sclera segmentation models. Although sclera segmentation techniques represent a key component of sclera-based biometric systems with a considerable impact on the overall recognition performance, the presence of different types of biases in sclera segmentation methods is still underexplored. To address this limitation, we describe the results of a group evaluation effort (involving seven research groups), organized to explore the performance of recent sclera segmentation models within a common experimental framework and study performance differences (and bias), originating from various demographic as well as environmental factors. Using five diverse datasets, we analyze seven independently developed sclera segmentation models in different experimental configurations. The results of our experiments suggest that there are significant differences in the overall segmentation performance across the seven models and that among the considered factors, ethnicity appears to be the biggest cause of bias. Additionally, we observe that training with representative and balanced data does not necessarily lead to less biased results. Finally, we find that in general there appears to be a negative correlation between the amount of bias observed (due to eye color, ethnicity and acquisition device) and the overall segmentation performance, suggesting that advances in the field of semantic segmentation may also help with mitigating bias.

In recent years, unconstraint iris biometric is becoming more prevalent due to its wide range of user applications. But since it allows less user co-operation, it presents numerous challenges to the iris preprocessing task of localisation and segmentation (ILS). To address these challenges, many ILS techniques have been proposed with the deep learning CNN based approaches been the most effective. Training the CNN is data intensive and most of the existing CNN based ILS adopt general purpose CNN without any iris specific guidance. However, the available iris dataset comprises of small subsets with labelled images. As such, the existing CNN models can be less effective as they are trained with these dataset. Hence, in this paper, we propose a guided CNN based ILS technique termed IrisGuideNet by incorporating known iris specific heuristics into the network pipeline. IrisGuideNet has an encoder-decoder structure designed to be invariant to translation and rotation and can capture iris at multiple scales. To address the iris limited data problem, unlike the existing CNN based ILS, during the training process, we adopt the deep supervision technique, employ hybrid losses and introduce a novel iris specific heuristics named Iris Regularization Term (IRT) in other to effectively train the network. At inference, we introduce a novel Iris Infusion Module (IIM) that utilise the geometrical relationships between the ILS outputs to refine the predicted outputs through logical operations. Our models were trained and evaluated with the recently published NIR-ISL Challange * datasets and has proven to be effective as it has outperformed most of the participating models across all the database categories in the competition.

This paper investigates a deep learning based unified framework for accurate sclera segmentation and recognition, named Sclera-TransFuse. Unlike previous CNN-based methods, our framework incorporates Vision Transformer and CNN to extract complementary feature representations, which are beneficial to both subtasks. Specifically, for sclera segmentation, a novel two-stream hybrid model, referred to as Sclera-TransFuse-Seg, is developed to integrate classical ResNet-34 and recently emerging Swin Transformer encoders in parallel. The dual-encoders firstly extract coarse-and fine-grained feature representations at hierarchical stages, separately. Then a Cross-Domain Fusion (CDF) module based on information interaction and self-attention mechanism is introduced to efficiently fuse the multi-scale features extracted from dual-encoders. Finally, the fused features are progressively upsampled and aggregated to predict the sclera masks in the decoder meanwhile deep supervision strategies are employed to learn intermediate feature representations better and faster. With the results of sclera segmentation, the sclera ROI image is generated for sclera feature extraction. Additionally, a new sclera recognition model, termed as Sclera-TransFuse-Rec, is proposed by combining lightweight EfficientNet B0 and multi-scale Vision Transformer in sequential to encode local and global sclera vasculature feature representations. Extensive experiments on several publicly available databases suggest that our framework consistently achieves state-of-the-art performance on various sclera segmentation and recognition benchmarks, including the 8th Sclera Segmentation and Recognition Benchmarking Competition (SSRBC 2023). A UBIRIS.v2 subset of 683 eye images with manually labeled sclera masks, and our codes are publicly available to the community through https://github.com/lhqqq/Sclera-TransFuse.