While Vision Transformer (ViT)-based methods have significantly improved the performance of various vision tasks in natural scenes, progress in iris recognition remains limited. In addition, the human iris contains unique characters that are distinct from natural scenes. To remedy this, this paper investigates a dedicated Transformer framework, termed IrisFormer, for iris recognition and attempts to improve the accuracy by combining the contextual modeling ability of ViT and iris-specific optimization to learn robust, fine-grained, and discriminative features. Specifically, to achieve rotation invariance in iris recognition, we employ relative position encoding instead of regular absolute position encoding for each iris image token, and a horizontal pixel-shifting strategy is utilized during training for data augmentation. Then, to enhance the model's robustness against local distortions such as occlusions and reflections, we randomly mask some tokens during training to force the model to learn representative identity features from only part of the image. Finally, considering that fine-grained features are more discriminative in iris recognition, we retain the entire token sequence for patch-wise feature matching instead of using the standard single classification token. Experiments on three popular datasets demonstrate that the proposed framework achieves competitive performance under both intra- and inter-dataset testing protocols.

This paper presents a summary of the Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Data (SynFacePAD 2023) held at the 2023 International Joint Conference on Biometrics (IJCB 2023). The competition attracted a total of 8 participating teams with valid submissions from academia and industry. The competition aimed to motivate and attract solutions that target detecting face presentation attacks while considering synthetic-based training data motivated by privacy, legal and ethical concerns associated with personal data. To achieve that, the training data used by the participants was limited to synthetic data provided by the organizers. The submitted solutions presented innovations and novel approaches that led to outperforming the considered baseline in the investigated benchmarks.

This paper presents the summary report on the DeepFake Game Competition on Visual Realism Assessment (DFGC-VRA). Deep-learning based face-swap videos, also known as deepfakes, are becoming more and more realistic and deceiving. The malicious usage of these face-swap videos has caused wide concerns. There is a ongoing deepfake game between its creators and detectors, with the human in the loop. The research community has been focusing on the automatic detection of these fake videos, but the assessment of their visual realism, as perceived by human eyes, is still an unexplored dimension. Visual realism assessment, or VRA, is essential for assessing the potential impact that may be brought by a specific face-swap video, and it is also useful as a quality metric to compare different face-swap methods. This is the third edition of DFGC competitions, which focuses on the new visual realism assessment topic, different from previous ones that compete creators versus detectors. With this competition, we conduct a comprehensive study of the SOTA performance on the new task. We also release our MindSpore codes to further facilitate research in this field (https://github.com/bomb2peng/DFGC-VRA-benckmark).

This paper describes the results of the 2023 edition of the “LivDet” series of iris presentation attack detection (PAD) competitions. New elements in this fifth competition include (1) GAN-generated iris images as a category of presentation attack instruments (PAI), and (2) an evaluation of human accuracy at detecting PAI as a reference benchmark. Clarkson University and the University of Notre Dame contributed image datasets for the competition, composed of samples representing seven different PAI categories, as well as baseline PAD algorithms. Fraunhofer IGD, Beijing University of Civil Engineering and Architecture, and Hochschule Darmstadt contributed results for a total of eight PAD algorithms to the competition. Accuracy results are analyzed by different PAI types, and compared to human accuracy. Overall, the Fraunhofer IGD algorithm, using an attention-based pixel-wise binary supervision network, showed the best-weighted accuracy results (average classification error rate of 37.31%), while the Beijing University of Civil Engineering and Architecture’s algorithm won when equal weights for each PAI were given (average classification rate of 22.15%). These results suggest that iris PAD is still a challenging problem.