Electrocardiography (ECG) is a widely used technique for recording the electrical activity of the heart. ECG measures voltage changes via electrodes on the body to reveal heart rhythms and potential cardiovascular issues. Yet, traditional ECG monitoring needs skin contact, specialized devices, and clinical oversight, limiting its use for daily or long-term monitoring. Remote electrocardiography is an emerging method for estimating ECG signals without any physical contact, using only visual information such as facial videos. Unlike traditional ECG systems that rely on adhesive electrodes and specialized equipment, rECG allows for contactless cardiac monitoring, making it well-suited for telemedicine, long-term health tracking, and unobtrusive assessments. In this work, we introduce the Guided ECG Diffusion Model (GEDM), a new framework that reconstructs accurate ECG signals from facial videos. GEDM combines a modified PhysNet to extract strong rPPG signals with a multi-stage diffusion process, guided by important ECG landmarks like the P wave, QRS complex, and T wave. This guidance ensures that the generated signals maintain high physiological accuracy. We also present the PhysioFace Diverse Dataset (PFDD), a large dataset containing synchronized facial videos and ECG signals from 100 subjects with diverse skin tones. Extensive experiments on both PFDD and the public MAHNOB-HCI dataset show that GEDM outperforms baseline models across multiple metrics, including MAE, RMSE, correlation, and SNR. These results set a new benchmark for contactless ECG generation and demonstrate the potential of rECG as an ECG-based biometric system or liveness detection technique for anti-spoofing biometric applications.

Contactless fingerprint recognition offers a higher level of user comfort and addresses hygiene concerns more effectively. However, it is also more vulnerable to presentation attacks, such as photo-paper, paper printout, and various display attacks, making it more challenging to implement in biometric systems compared to contact-based modalities. Limited research has been conducted on presentation attacks in contactless fingerprint systems, and these studies have encountered challenges in terms of generalization and scalability since both bonafide samples and presentation attacks are utilized during the training model. Although this approach appears promising, it lacks the ability to handle unseen attacks, which is a crucial factor for developing PAD methods that can generalize effectively. We introduced an innovative anti-spoofing approach that combines an unsupervised autoencoder with a convolutional block attention module to address the limitations of existing methods. Our model is trained exclusively on bonafide images without exposure to any spoofed samples during the training phase. It is then evaluated against various types of presentation attack images in the testing phase. The scheme we proposed has achieved an average BPCER of 0.96% with an APCER of 1.6% for presentation attacks involving various types of spoofed samples.

Imagine a world where a copy of your face could trick the most advanced security systems. This isn’t science fiction; it’s a real challenge today. LivDet-Face is a competition that aims to advance the detection of attacks at the biometric sensor, known as Presentation Attack Detection (PAD). This international contest is a key benchmark in biometric security, offering an unbiased look at the latest innovations in face PAD and demonstrating progress over time in detecting and preventing sophisticated attacks. Through the International Joint Conference on Biometrics (IJCB) platform, LivDet-Face 2024 provides a standardized evaluation process, access to advanced Presentation Attack Instruments (PAI), and a comprehensive dataset of bona fide face images. The competition had two main categories: algorithms and systems. A total of sixteen algorithms and one system were submitted for this year’s competition. Anonymous submissions topped both image and video subcategories with an ACER of 4.93% and 4.13%, respectively. In the systems category, Team Dermalog, despite being the sole submission, achieved an impressive ACER of 3.12%.

With the increasing integration of smartphones into our daily lives, fingerphotos are becoming a potential contactless authentication method. While it offers convenience, it is also more vulnerable to spoofing using various presentation attack instruments (PAI). The contactless fingerprint is an emerging biometric authentication but has not yet been heavily investigated for anti-spoofing. While existing anti-spoofing approaches demonstrated fair results, they have encountered challenges in terms of universality and scalability to detect any unseen/unknown spoofed samples. To address this issue, we propose a universal presentation attack detection method for contactless fingerprints, despite having limited knowledge of presentation attack samples. We generated synthetic contactless fingerprints using StyleGAN from live finger photos and integrating them to train a semi-supervised ResNet-18 model. A novel joint loss function, combining the Arcface and Center loss, is introduced with a regularization to balance between the two loss functions and minimize the variations within the live samples while enhancing the inter-class variations between the deepfake and live samples. We also conducted a comprehensive comparison of different regularizations’ impact on the joint loss function for presentation attack detection (PAD) and explored the performance of a modified ResNet-18 architecture with different activation functions (i.e., leaky ReLU and RelU) in conjunction with Arcface and center loss. Finally, we evaluate the performance of the model using unseen types of spoof attacks and live data. Our proposed method achieves a Bona Fide Classification Error Rate (BPCER) of 0.12%, an Attack Presentation Classification Error Rate (APCER) of 0.63%, and an Average Classification Error Rate (ACER) of 0.37%.

Liveness Detection (LivDet) is an international competition series open to academia and industry with the objective to assess and report state-of-the-art in Presentation Attack Detection (PAD). LivDet-2023 Noncontact Fingerprint is the first edition of the noncontact fingerprint-based PAD competition for algorithms and systems. The competition serves as an important benchmark in noncontact-based fingerprint PAD, offering (a) independent assessment of the state-of-the-art in noncontact-based fingerprint PAD for algorithms and systems, and (b) common evaluation protocol, which includes finger photos of a variety of Presentation Attack Instruments (PAIs) and live fingers to the biometric research community (c) provides standard algorithm and system evaluation protocols, along with the comparative analysis of state-of-the-art algorithms from academia and industry with both old and new android smartphones. The winning algorithm achieved an APCER of 11.35% averaged over all PAIs and a BPCER of 0.62%. The winning system achieved an APCER of 13.0.4%, averaged over all PAIs tested over all the smartphones, and a BPCER of 1.68% over all smartphones tested. Four-finger systems that make individual finger-based PAD decisions were also tested. The dataset used for competition will be available1, to all researchers as per data share protocol.1https://noncontactfingerprint2023.1ivdet.org/index.php