Synthetic data has emerged as a promising alternative for training face recognition (FR) models, offering advantages in scalability, privacy compliance, and potential for bias mitigation. However, critical questions remain on whether both high accuracy and fairness can be achieved with synthetic data. In this work, we evaluate the impact of synthetic data on bias and performance of FR systems. We generate balanced face dataset, FairFaceGen, using two state of the art text-to-image generators, Flux.1-dev and Stable Diffusion v3.5 (SD35), and combine them with several identity augmentation methods, including Arc2Face and four IP-Adapters. By maintaining equal identity count across synthetic and real datasets, we ensure fair comparisons when evaluating FR performance on standard (LFW, AgeDB-30, etc.) and challenging IJB-B/C benchmarks and FR bias on Racial Faces in-the-Wild (RFW) dataset. Our results demonstrate that although synthetic data still lags behind the real datasets in the generalization on IJB-B/C, demographically balanced synthetic datasets, especially those generated with SD35, show potential for bias mitigation. We also observe that the number and quality of intra-class augmentations significantly affect FR accuracy and fairness. These findings provide practical guidelines for constructing fairer FR systems using synthetic data.

This work summarises and reports the results of the second Presentation Attack Detection competition on ID cards. This new version includes new elements compared to the previous one. (1) An automatic evaluation platform was enabled for automatic benchmarking; (2) Two tracks were proposed in order to evaluate algorithms and datasets respectively; and (3) A new ID card dataset was shared with Track 1 teams to serve as the baseline dataset for the training and optimisation. The Hochschule Darmstadt, Fraunhofer-IGD, and Facephi company jointly organised this challenge. 20 teams were registered, and 74 submitted models were evaluated. For Track 1, the "Dragons" team reached first place with an Average Ranking and Equal Error rate (EER) of (AVRank) of 40.48% and 11.44% EER, respectively. For the more challenging approach in Track 2, the "Incode" team reached the best results with an AVRank of 14.76% and 6.36% EER, improving on the results of the first edition of 74.30% and 21.87% EER, respectively. These results suggest that PAD on ID cards is improving, but it is still a challenging problem related to the number of images, especially of bona fide images.

Demographic bias in deep learning-based face recognition systems has led to serious concerns. Often, the biased nature of models is attributed to severely imbalanced datasets used for training. However, several studies have shown that biased models can emerge even when trained on balanced data due to factors in the data acquisition process. Considering the impact of input data on demographic bias, we propose an image to image transformer for demographic fairness (DeFT). This transformer can be applied before the pretrained recognition CNN to selectively enhance the image representation with the goal of reducing the bias through overall recognition pipeline. The multi-head encoders of DeFT provide multiple transformation paths to the input which are then combined based on its demographic information implicitly inferred through soft-attention mechanism applied to intermittent layers of DeFT. We compute probabilistic weights for demographic information, as opposed to conventional hard labels, simplifying the learning process and enhancing the robustness of the DeFT. Our experiments demonstrate that in a cross-dataset testing (pretrained as well as locally trained models), integrating the DeFT leads to fairer models, reducing the variation in accuracies while often slightly improving average recognition accuracy over baselines.

Recent developments in Virtual Reality (VR) headsets have unlocked a plethora of innovative use-cases, many of which were previously unimaginable. However, as these use-cases, such as personalized immersive experiences, necessitate user authentication, ensuring robustness and resistance to spoofing attacks becomes imperative. The absence of appropriate dataset has constrained our understanding and assessment of VR devices’ susceptibility to presentation attacks. To address this research gap, we introduce VRBiom: a new periocular video dataset acquired from a VR headset (Meta Quest Pro), comprising 900 genuine and 1104 presentation attack videos, each spanning 10 seconds. The bona-fide videos consist of variations in terms of gaze and glasses; while the attacks are constructed with 6 different types of instruments. Additionally, we evaluate the performance of two prominent CNN architectures trained using various configurations for detecting presentation attacks in the newly created VRBiom dataset. Our benchmarking on VRBiom reveals the presence of spoofing threats in VR headsets. While baseline models exhibit considerable efficacy in attack detection, substantial scope exists for improvement in detecting attacks on periocular videos. Our dataset will be a useful resource for researchers aiming to enhance the security and reliability of VR-based authentication systems.

Large occlusions result in a significant decline in image classification accuracy. During inference, diverse types of unseen occlusions introduce out-of-distribution data to the classification model, leading to accuracy dropping as low as 50%. As occlusions encompass spatially connected regions, conventional methods involving feature reconstruction are inadequate for enhancing classification performance. We introduce LEARN: Latent Enhancing feAture Reconstruction Network-An auto-encoder based network that can be incorporated into the classification model before its classifier head without modifying the weights of classification model. In addition to reconstruction and classification losses, training of LEARN effectively combines intra-and inter-class losses calculated over its latent space-which lead to improvement in recovering latent space of occluded data, while preserving its class-specific discriminative information. On the OccludedPASCAL3D+ dataset, the proposed LEARN outperforms standard classification models (VGG16 and ResNet-50) by a large margin and up to 2% over state-of-the-art methods. In cross-dataset testing, our method improves the average classification accuracy by more than 5 2% over the state-of-the-art methods. In every experiment, our model consistently maintains excellent accuracy on in-distribution data.

This paper presents the summary of the Efficient Face Recognition Competition (EFaR) held at the 2023 International Joint Conference on Biometrics (IJCB 2023). The competition received 17 submissions from 6 different teams. To drive further development of efficient face recognition models, the submitted solutions are ranked based on a weighted score of the achieved verification accuracies on a diverse set of benchmarks, as well as the deployability given by the number of floating-point operations and model size. The evaluation of submissions is extended to bias, cross-quality, and large-scale recognition benchmarks. Overall, the paper gives an overview of the achieved performance values of the submitted solutions as well as a diverse set of baselines. The submitted solutions use small, efficient network architectures to reduce the computational cost, some solutions apply model quantization. An outlook on possible techniques that are underrepresented in current solutions is given as well.

Presentation attacks using 3D masks pose a serious threat to face recognition systems. Automatic detection of these attacks is challenging due to hyper-realistic nature of masks. In this work, we consider presentations acquired in near infrared (NIR) imaging channel for detection of mask-based attacks. We propose a patch pooling mechanism to learn complex textural features from lower layers of a convolutional neural network (CNN). The proposed patch pooling layer can be used in conjunction with a pretrained face recognition CNN without fine-tuning or adaptation. The pretrained CNN, in fact, can also be trained from visual spectrum data. We demonstrate efficacy of the proposed method on mask attacks in NIR channel from WMCA and MLFP datasets. It achieves near perfect results on WMCA data, and outperforms existing benchmark on MLFP dataset by a large margin.

This paper presents a new approach for hyperspectral image visualization. A bilateral filtering-based approach is presented for hyperspectral image fusion to generate an appropriate resultant image. The proposed approach retains even the minor details that exist in individual image bands, by exploiting the edge-preserving characteristics of a bilateral filter. It does not introduce visible artifacts in the fused image. A hierarchical fusion scheme has also been proposed for implementation purposes to accommodate a large number of hyperspectral image bands. The proposed scheme provides computational and storage efficiency without affecting the quality and performance of the fusion. It also facilitates the midband visualization of a subset of the hyperspectral image cube. Quantitative performance results are presented to indicate the effectiveness of the proposed method.