Abstract
In the context of biometrics, matching confidence refers to the confidence that a given matching decision is correct. Since many biometric systems operate in critical decision-making processes, such as in forensics investigations, accurately and reliably stating the matching confidence becomes of high importance. Previous works on biometric confidence estimation can well differentiate between high and low confidence, but lack interpretability. Therefore, they do not provide accurate probabilistic estimates of the correctness of a decision. In this work, we propose a probabilistic interpretable comparison (PIC) score that accurately reflects the probability that the score originates from samples of the same identity. We prove that the proposed approach provides optimal matching confidence. Contrary to other approaches, it can also optimally combine multiple samples in a joint PIC score which further increases the recognition and confidence estimation performance. In the experiments, the proposed PIC approach is compared against all biometric confidence estimation methods available on four publicly available databases and five state-of-the-art face recognition systems. The results demonstrate that PIC has a significantly more accurate probabilistic interpretation than similar approaches and is highly effective for multi-biometric recognition. The code is publicly-available1. © 2023 IEEE.

Abstract
Presentation attacks are recurrent threats to biometric systems, where impostors attempt to bypass these systems. Humans often use background information as contextual cues for their visual system. Yet, regarding face-based systems, the background is often discarded, since face presentation attack detection (PAD) models are mostly trained with face crops. This work presents a comparative study of face PAD models (including multi-task learning, adversarial training and dynamic frame selection) in two settings: with and without crops. The results show that the performance is consistently better when the background is present in the images. The proposed multi-task methodology beats the state-of-the-art results on the ROSE-Youtu dataset by a large margin with an equal error rate of 0.2%. Furthermore, we analyze the models' predictions with Grad-CAM++ with the aim to investigate to what extent the models focus on background elements that are known to be useful for human inspection. From this analysis we can conclude that the background cues are not relevant across all the attacks. Thus, showing the capability of the model to leverage the background information only when necessary.

Abstract

Abstract
Over the years, the evolution of face recognition (FR) algorithms has been steep and accelerated by a myriad of factors. Motivated by the unexpected elements found in real-world scenarios, researchers have investigated and developed a number of methods for occluded face recognition (OFR). However, due to the SarS-Cov2 pandemic, masked face recognition (MFR) research branched from OFR and became a hot and urgent research challenge. Due to time and data constraints, these models followed different and novel approaches to handle lower face occlusions, i.e., face masks. Hence, this study aims to evaluate the different approaches followed for both MFR and OFR, find linked details about the two conceptually similar research directions and understand future directions for both topics. For this analysis, several occluded and face recognition algorithms from the literature are studied. First, they are evaluated in the task that they were trained on, but also on the other. These methods were picked accordingly to the novelty of their approach, proven state-of-the-art results, and publicly available source code. We present quantitative results on 4 occluded and 5 masked FR datasets, and a qualitative analysis of several MFR and OFR models on the Occ-LFW dataset. The analysis presented, sustain the interoperable deployability of MFR methods on OFR datasets, when the occlusions are of a reasonable size. Thus, solutions proposed for MFR can be effectively deployed for general OFR.

Abstract
Morphing attacks are one of the many threats that are constantly affecting deep face recognition systems. It consists of selecting two faces from different individuals and fusing them into a final image that contains the identity information of both. In this work, we propose a novel regularisation term that takes into account the existent identity information in both and promotes the creation of two orthogonal latent vectors. We evaluate our proposed method (OrthoMAD) in five different types of morphing in the FRLL dataset and evaluate the performance of our model when trained on five distinct datasets. With a small ResNet-18 as the backbone, we achieve state-of-the-art results in the majority of the experiments, and competitive results in the others.