LD-ViCE: Latent Diffusion Model for Video Counterfactual Explanations

Video-based AI systems are increasingly adopted in safety-critical domains such as autonomous driving and healthcare. However, interpreting their decisions remains challenging due to the inherent spatiotemporal complexity of video data and the opacity of deep learning models. Existing explanation techniques often suffer from limited temporal coherence, insufficient robustness, and a lack of actionable causal insights. Current counterfactual explanation methods typically do not incorporate guidance from the target model, reducing semantic fidelity and practical utility. We introduce Latent Diffusion for Video Counterfactual Explanations (LD-ViCE), a novel framework designed to explain the behavior of video-based AI models. Compared to previous approaches, LD-ViCE reduces the computational costs of generating explanations by operating in latent space using a state-of-the-art diffusion model, while producing realistic and interpretable counterfactuals through an additional refinement step. Our experiments demonstrate the effectiveness of LD-ViCE across three diverse video datasets, including EchoNet-Dynamic (cardiac ultrasound), FERV39k (facial expression), and Something-Something V2 (action recognition). LD-ViCE outperforms a recent state-of-the-art method, achieving an increase in R2 score of up to 68% while reducing inference time by half. Qualitative analysis confirms that LD-ViCE generates semantically meaningful and temporally coherent explanations, offering valuable insights into the target model behavior. LD-ViCE represents a valuable step toward the trustworthy deployment of AI in safety-critical domains.

Skeleton-based sign language recognition using a dual-stream spatio-temporal dynamic graph convolutional network

Isolated Sign Language Recognition (ISLR) is challenged by gestures that are morphologically similar yet semantically distinct, a problem rooted in the complex interplay between hand shape and motion trajectory. Existing methods, often relying on a single reference frame, struggle to resolve this geometric ambiguity. This paper introduces Dual-SignLanguageNet (DSLNet), a dual-reference, dual-stream architecture that decouples and models gesture morphology and trajectory in separate, complementary coordinate systems. Our approach utilizes a wrist-centric frame for view-invariant shape analysis and a facial-centric frame for context-aware trajectory modeling. These streams are processed by specialized networks-a topology-aware graph convolution for shape and a Finsler geometry-based encoder for trajectory-and are integrated via a geometry-driven optimal transport fusion mechanism. DSLNet sets a new state-of-the-art, achieving 93.70%, 89.97% and 99.79% accuracy on the challenging WLASL-100, WLASL-300 and LSA64 datasets, respectively, with significantly fewer parameters than competing models.

MICACL: Multi-Instance Category-Aware Contrastive Learning for Long-Tailed Dynamic Facial Expression Recognition

Dynamic facial expression recognition (DFER) faces significant challenges due to long-tailed category distributions and complexity of spatio-temporal feature modeling. While existing deep learning-based methods have improved DFER performance, they often fail to address these issues, resulting in severe model induction bias. To overcome these limitations, we propose a novel multi-instance learning framework called MICACL, which integrates spatio-temporal dependency modeling and long-tailed contrastive learning optimization. Specifically, we design the Graph-Enhanced Instance Interaction Module (GEIIM) to capture intricate spatio-temporal between adjacent instances relationships through adaptive adjacency matrices and multiscale convolutions. To enhance instance-level feature aggregation, we develop the Weighted Instance Aggregation Network (WIAN), which dynamically assigns weights based on instance importance. Furthermore, we introduce a Multiscale Category-aware Contrastive Learning (MCCL) strategy to balance training between major and minor categories. Extensive experiments on in-the-wild datasets (i.e., DFEW and FERV39k) demonstrate that MICACL achieves state-of-the-art performance with superior robustness and generalization.

Addressing Deepfake Issue in Selfie banking through camera based authentication

Fake images in selfie banking are increasingly becoming a threat. Previously, it was just Photoshop, but now deep learning technologies enable us to create highly realistic fake identities, which fraudsters exploit to bypass biometric systems such as facial recognition in online banking. This paper explores the use of an already established forensic recognition system, previously used for picture camera localization, in deepfake detection.

Sistema de Reconocimiento Facial Federado en Conjuntos Abiertos basado en OpenMax

Facial recognition powered by Artificial Intelligence has achieved high accuracy in specific scenarios and applications. Nevertheless, it faces significant challenges regarding privacy and identity management, particularly when unknown individuals appear in the operational context. This paper presents the design, implementation, and evaluation of a facial recognition system within a federated learning framework tailored to open-set scenarios. The proposed approach integrates the OpenMax algorithm into federated learning, leveraging the exchange of mean activation vectors and local distance measures to reliably distinguish between known and unknown subjects. Experimental results validate the effectiveness of the proposed solution, demonstrating its potential for enhancing privacy-aware and robust facial recognition in distributed environments. -- El reconocimiento facial impulsado por Inteligencia Artificial ha demostrado una alta precisi\'on en algunos escenarios y aplicaciones. Sin embargo, presenta desaf\'ios relacionados con la privacidad y la identificaci\'on de personas, especialmente considerando que pueden aparecer sujetos desconocidos para el sistema que lo implementa. En este trabajo, se propone el dise\~no, implementaci\'on y evaluaci\'on de un sistema de reconocimiento facial en un escenario de aprendizaje federado, orientado a conjuntos abiertos. Concretamente, se dise\~na una soluci\'on basada en el algoritmo OpenMax para escenarios de aprendizaje federado. La propuesta emplea el intercambio de los vectores de activaci\'on promedio y distancias locales para identificar de manera eficaz tanto personas conocidas como desconocidas. Los experimentos realizados demuestran la implementaci\'on efectiva de la soluci\'on propuesta.

A holistic perception system of internal and external monitoring for ground autonomous vehicles: AutoTRUST paradigm

This paper introduces a holistic perception system for internal and external monitoring of autonomous vehicles, with the aim of demonstrating a novel AI-leveraged self-adaptive framework of advanced vehicle technologies and solutions that optimize perception and experience on-board. Internal monitoring system relies on a multi-camera setup designed for predicting and identifying driver and occupant behavior through facial recognition, exploiting in addition a large language model as virtual assistant. Moreover, the in-cabin monitoring system includes AI-empowered smart sensors that measure air-quality and perform thermal comfort analysis for efficient on and off-boarding. On the other hand, external monitoring system perceives the surrounding environment of vehicle, through a LiDAR-based cost-efficient semantic segmentation approach, that performs highly accurate and efficient super-resolution on low-quality raw 3D point clouds. The holistic perception framework is developed in the context of EU's Horizon Europe programm AutoTRUST, and has been integrated and deployed on a real electric vehicle provided by ALKE. Experimental validation and evaluation at the integration site of Joint Research Centre at Ispra, Italy, highlights increased performance and efficiency of the modular blocks of the proposed perception architecture.

Boosting Micro-Expression Analysis via Prior-Guided Video-Level Regression

Micro-expressions (MEs) are involuntary, low-intensity, and short-duration facial expressions that often reveal an individual's genuine thoughts and emotions. Most existing ME analysis methods rely on window-level classification with fixed window sizes and hard decisions, which limits their ability to capture the complex temporal dynamics of MEs. Although recent approaches have adopted video-level regression frameworks to address some of these challenges, interval decoding still depends on manually predefined, window-based methods, leaving the issue only partially mitigated. In this paper, we propose a prior-guided video-level regression method for ME analysis. We introduce a scalable interval selection strategy that comprehensively considers the temporal evolution, duration, and class distribution characteristics of MEs, enabling precise spotting of the onset, apex, and offset phases. In addition, we introduce a synergistic optimization framework, in which the spotting and recognition tasks share parameters except for the classification heads. This fully exploits complementary information, makes more efficient use of limited data, and enhances the model's capability. Extensive experiments on multiple benchmark datasets demonstrate the state-of-the-art performance of our method, with an STRS of 0.0562 on CAS(ME)$^3$ and 0.2000 on SAMMLV. The code is available at https://github.com/zizheng-guo/BoostingVRME.

VOIDFace: A Privacy-Preserving Multi-Network Face Recognition With Enhanced Security

Advancement of machine learning techniques, combined with the availability of large-scale datasets, has significantly improved the accuracy and efficiency of facial recognition. Modern facial recognition systems are trained using large face datasets collected from diverse individuals or public repositories. However, for training, these datasets are often replicated and stored in multiple workstations, resulting in data replication, which complicates database management and oversight. Currently, once a user submits their face for dataset preparation, they lose control over how their data is used, raising significant privacy and ethical concerns. This paper introduces VOIDFace, a novel framework for facial recognition systems that addresses two major issues. First, it eliminates the need of data replication and improves data control to securely store training face data by using visual secret sharing. Second, it proposes a patch-based multi-training network that uses this novel training data storage mechanism to develop a robust, privacy-preserving facial recognition system. By integrating these advancements, VOIDFace aims to improve the privacy, security, and efficiency of facial recognition training, while ensuring greater control over sensitive personal face data. VOIDFace also enables users to exercise their Right-To-Be-Forgotten property to control their personal data. Experimental evaluations on the VGGFace2 dataset show that VOIDFace provides Right-To-Be-Forgotten, improved data control, security, and privacy while maintaining competitive facial recognition performance. Code is available at: https://github.com/ajnasmuhammed89/VOIDFace

Deep Learning Models for Robust Facial Liveness Detection

In the rapidly evolving landscape of digital security, biometric authentication systems, particularly facial recognition, have emerged as integral components of various security protocols. However, the reliability of these systems is compromised by sophisticated spoofing attacks, where imposters gain unauthorized access by falsifying biometric traits. Current literature reveals a concerning gap: existing liveness detection methodologies - designed to counteract these breaches - fall short against advanced spoofing tactics employing deepfakes and other artificial intelligence-driven manipulations. This study introduces a robust solution through novel deep learning models addressing the deficiencies in contemporary anti-spoofing techniques. By innovatively integrating texture analysis and reflective properties associated with genuine human traits, our models distinguish authentic presence from replicas with remarkable precision. Extensive evaluations were conducted across five diverse datasets, encompassing a wide range of attack vectors and environmental conditions. Results demonstrate substantial advancement over existing systems, with our best model (AttackNet V2.2) achieving 99.9% average accuracy when trained on combined data. Moreover, our research unveils critical insights into the behavioral patterns of impostor attacks, contributing to a more nuanced understanding of their evolving nature. The implications are profound: our models do not merely fortify the authentication processes but also instill confidence in biometric systems across various sectors reliant on secure access.

The Importance of Facial Features in Vision-based Sign Language Recognition: Eyes, Mouth or Full Face?

Non-manual facial features play a crucial role in sign language communication, yet their importance in automatic sign language recognition (ASLR) remains underexplored. While prior studies have shown that incorporating facial features can improve recognition, related work often relies on hand-crafted feature extraction and fails to go beyond the comparison of manual features versus the combination of manual and facial features. In this work, we systematically investigate the contribution of distinct facial regionseyes, mouth, and full faceusing two different deep learning models (a CNN-based model and a transformer-based model) trained on an SLR dataset of isolated signs with randomly selected classes. Through quantitative performance and qualitative saliency map evaluation, we reveal that the mouth is the most important non-manual facial feature, significantly improving accuracy. Our findings highlight the necessity of incorporating facial features in ASLR.