This paper presents a modular augmented reality (AR) framework designed to support healthcare professionals in the real-time visualization and interaction with clinical data. The system integrates biometric patient identification, large language models (LLMs) for multimodal clinical data structuring, and ontology-driven AR overlays for anatomy-aware spatial projection. Unlike conventional systems, the framework enables immersive, context-aware visualization that improves both the accessibility and interpretability of medical information. The architecture is fully modular and mobile-compatible, allowing independent refinement of its core components. Patient identification is performed through facial recognition, while clinical documents are processed by a vision-language pipeline that standardizes heterogeneous records into structured data. Body-tracking technology anchors these parameters to the corresponding anatomical regions, supporting intuitive and dynamic interaction during consultations. The framework has been validated through a diabetology case study and a usability assessment with five clinicians, achieving a System Usability Scale (SUS) score of 73.0, which indicates good usability. Experimental results confirm the accuracy of biometric identification (97.1%). The LLM-based pipeline achieved an exact match accuracy of 98.0% for diagnosis extraction and 86.0% for treatment extraction from unstructured clinical images, confirming its reliability in structuring heterogeneous medical content. The system is released as open source to encourage reproducibility and collaborative development. Overall, this work contributes a flexible, clinician-oriented AR platform that combines biometric recognition, multimodal data processing, and interactive visualization to advance next-generation digital healthcare applications. • Modular AR system for real-time clinical data visualization and interaction. • Clinical data mapped to anatomical regions via ontology-guided AR overlay. • Structured clinical data extraction via mobile-efficient multimodal LLMs.

Soft Facial Attribute Recognition (FAR) remains largely unexplored in terms of demographic fairness. To the best of our knowledge, this study presents one of the first comprehensive analyses of demographic bias in FAR, proposing a systematic framework to detect, quantify, and promote awareness of both representational and stereotypical biases, supporting their mitigation. Leveraging established taxonomies, we evaluate state-of-the-art datasets using a rigorous set of interpretable bias metrics to uncover hidden demographic imbalances. To support reliable fairness assessment, we first enrich the datasets with standardized demographic annotations using the FairFace model. We then address label inconsistencies through the integration of predictions from advanced Vision-Language Models (VLMs). Our analysis reveals substantial imbalances across gender, age, and racial categories-specifically White, Black, and Asian- affecting dataset composition. Furthermore, we show that conventional fairness metrics often yield divergent assessments, highlighting the importance of multi-metric evaluation. This study provides a replicable methodology and actionable insights to support bias-aware facial analysis.

There is a rising concern about healthcare system security, where data loss could bring lots of damages to patients and hospitals. As a promising encryption method for medical images, DNA encoding own characteristics of high speed, parallelism computation, minimal storage, and unbreakable cryptosystems. Inspired by the idea of involving Large Language Models(LLMs) to improve DNA encoding, we propose a medical image encryption method with LLM-enhanced DNA encoding, which consists of LLM enhancing module and content-aware permutation&diffusion module. Regarding medical images generally have plain backgrounds with low-entropy pixels, the first module compresses pixels into highly compact signals with features of probabilistic varying and plausibly deniability, serving as another LLM-based layer of defense against privacy breaches before DNA encoding. The second module not only adds permutation by randomly sampling from a redundant correlation between adjacent pixels to break the internal links between pixels but also performs a DNA-based diffusion process to greatly increase the complexity of cracking. Experiments on ChestXray-14, COVID-CT and fcon-1000 datasets show that the proposed method outperforms all comparative methods in sensitivity, correlation and entropy.

Bias analysis for synthetic face detection is bound to become a critical topic in the coming years. Although many detection models have been developed and several datasets have been released to reliably identify synthetic content, one crucial aspect has been largely overlooked: these models and training datasets can be biased, leading to failures in detection for certain demographic groups and raising significant social, legal, and ethical issues. In this work, we introduce an evaluation framework to contribute to the analysis of bias of synthetic face detectors with respect to several facial attributes. This framework exploits synthetic data generation, with evenly distributed attribute labels, for mitigating any skew in the data that could otherwise influence the outcomes of bias analysis. We build on the proposed framework to provide an extensive case study of the bias level of five state-of-the-art detectors in synthetic datasets with 25 controlled facial attributes. While the results confirm that, in general, synthetic face detectors are biased towards the presence/absence of specific facial attributes, our study also sheds light on the origins of the observed bias through the analysis of the correlations with the balancing of facial attributes in the training sets of the detectors, and the analysis of detectors activation maps in image pairs with controlled attribute modifications.

Head pose estimation (HPE) is crucial in various real-world applications, like human–computer interaction and biometric framework enhancement. This research aims to leverage network curvature to predict head pose from a single image. In networks, certain groups of nodes fulfill significant functional roles. This study focuses on the interactions of facial landmarks, considered as vertices in a weighted graph. The experiments demonstrate that the underlying graph geometry and topology enable the detection of similarities among various head poses. Two independent notions of discrete Ricci curvature for graphs, namely Ollivier–Ricci and Forman–Ricci curvatures, are investigated. These two types of Ricci curvature, each reflecting distinct geometric properties of the network, serve as inputs to the regression model. The results from the BIWI, AFLW2000, and Pointing‘04 datasets reveal that the two discretizations of Ricci’s curvature are closely related and outperform state-of-the-art methods, including both landmark-based and image-only approaches. This demonstrates the effectiveness and promise of using network curvature for HPE in diverse applications. • The topology of the underlying graph can identify similarities across head poses. • Analyzes the performance differences between Ollivier and Forman Ricci curvatures. • Ollivier–Ricci-based method shows competitive results on three different datasets. • The Forman–Ricci-based method offers a computationally efficient alternative.

Online platforms are nurturing social interactions, yet regrettably, they have also led to the proliferation of antisocial behaviors such as cyberbullying, trolling, and hate speech on a global scale. The identification of hate speech and aggression has become indispensable in the fight against cyberbullying and online harassment. Cyberbullying encompasses the use of aggressive and offensive language, including rude, insulting, hateful, and teasing comments, to inflict harm on individuals through social media platforms. Human moderation is both sluggish and costly, rendering it impractical in light of the exponential growth of data. Consequently, automated detection systems are imperative to effectively combat trolling. This study addresses the challenge of automatically discerning cyberbullying in tweets sourced from a publicly available cyberbullying dataset. The proposed methodology leverages the robustly optimized bidirectional encoder representations from transformers approach (RoBERTa), integrating principle component analysis (PCA) extracted global vectors for word representation (GLOVE) word embedding features. Furthermore, our proposed approach is benchmarked against state-of-the-art machine learning, deep learning, and transformer-based methods, utilizing the GLOVE word embedding technique. Statistical analyses reveal that our proposed model outperforms its counterparts, achieving a 0.98 accuracy and recall rate with 0.97 of precision and F1 score in detecting cyberbullying tweets. Results from$k$-fold cross validation further corroborate the superior performance of our proposed model.

Diabetes mellitus is one of the most prevalent chronic diseases, affecting millions of people worldwide. Effective management of diabetes, particularly type 1 (T1DM) and type 2 diabetes (T2DM), requires a deep understanding of the complex interactions between clinical, behavioral, and socio-demographic factors. This study leverages Bayesian networks (BNs) to model these interactions, providing a transparent and interpretable visual framework that reveals how variables such as insulin use, BMI, and socio-economic status influence diabetes outcomes. However, constructing the graphical structure of a BN poses significant challenges due to the intricate and multifaceted relationships involved. To ensure a meaningful comparison between T1DM and T2DM, we utilized a cohort of subjects selected to be as demographically homogeneous as possible. This allowed us to reduce confounding effects and focus on the intrinsic differences between the two conditions. We compared network structures derived from three data samples (50%, 80%, and 100%) to explore how variable relationships evolve as the dataset size increases, ensuring that critical interactions were captured at different levels of data availability. The findings highlight key differences in the management of T1DM and T2DM, particularly with regard to behavioral and socio-economic factors.

Emotion recognition from facial expressions is a fundamental human ability that can be harnessed and transferred to machines. The ability to differentiate between spontaneous and posed emotions holds significant importance in various domains, including behavioral biometrics, forensics, and security. This paper introduces a novel method, called POsed vs Spontaneous Emotion Recognition (POSER), which leverages a modified version of the Partitioned Iterated Functions System (PIFS) to obtain a Fractal Encoding. This encoding is used for the first time as facial features to train a machine learning approach for the classification of emotions as either spontaneous or posed. Furthermore, by adapting the original architecture, we demonstrate the effectiveness of these features in distinguishing seven different emotions in controlled as well as wild environments, within a framework referred to as POSER-EMO. Experimental results are presented on the SPOS and DISFA + datasets for the first classification problem, where POSER outperforms the state of the art, and on the CK + and SFEW datasets for the second classification problem.

Advanced Driver Assistance Systems (ADAS) are experiencing higher levels of automation, facilitated by the synergy among various sensors integrated within vehicles, thereby forming an Internet of Things (IoT) framework. Among these sensors, cameras have emerged as valuable tools for detecting driver fatigue and distraction. This study introduces HYDE-F, a Head Pose Estimation (HPE) system exclusively utilizing depth cameras. HYDE-F adeptly identifies critical driver head poses associated with risky conditions, thus enhancing the safety of IoT-enabled ADAS. The core of HYDE-F’s innovation lies in its dual-process approach: it employs a fractal encoding technique and keypoint intensity analysis in parallel. These two processes are then fused using an optimization algorithm, enabling HYDE-F to blend the strengths of both methods for enhanced accuracy. Evaluations conducted on a specialized driving dataset, Pandora, demonstrate HYDE-F’s competitive performance compared to existing methods, surpassing current techniques in terms of average Mean Absolute Error (MAE) by nearly 1 ∘ . Moreover, case studies highlight the successful integration of HYDE-F with vehicle sensors. Additionally, HYDE-F exhibits robust generalization capabilities, as evidenced by experiments conducted on standard laboratory-based HPE datasets, i.e., Biwi and ICT-3DHP databases, achieving an average MAE of 4.9 ∘ and 5 ∘ , respectively.

Language has always been one of humanity's defining characteristics. Visual Language Identification (VLI) is a relatively new field of research that is complex and largely understudied. In this paper, we present a preliminary study in which we use linguistic information as a soft biometric trait to enhance the performance of a visual (auditory-free) identification system based on lip movement. We report a significant improvement in the identification performance of the proposed visual system as a result of the integration of these data using a score-based fusion strategy. Methods of Deep and Machine Learning are considered and evaluated. To the experimentation purposes, the dataset called laBial Articulation for the proBlem of the spokEn Language rEcognition (BABELE), consisting of 8 different languages, has been created. It includes a collection of different features of which the spoken language represents the most relevant, while each sample is also manually labelled with gender and age of the subjects.

The decisions behind the mechanics of a biometric verification system based on Machine Learning (ML) are difficult to comprehend. Although there is now well-established research in various fields of application, such as health or justice, the use of ML-based methods is accompanied by a lack of confidence that results in their limited use. The explainability of a ML system and the comprehension of what lies behind its prediction is one of the numerous characteristics that define “trust” in these systems. Over the years, face-based biometric authentication has been the subject of extensive research in both academia and industry. However, existing biometric authentication systems still have problems regarding accuracy, robustness and, explainability. Still lacking in the literature is a comprehensive examination of the use of post-hoc explainability techniques for such systems. Cognitive neuroscience has always been interested in the method by which people perceive faces; local elements such as the nose, eyes, and mouth are critical to the perception and recognition of a face. In this work, starting from this assumption, we propose a framework of visual and textual explainability based on the parts of a face by analyzing them with respect to the facial attributes reported in the CelebA dataset. The primary objective is to be able to explain why two pictures of different subjects are distinct. This is done by sinthesizing pairs of images that illustrate how dissimilar the various parts of the face under investigation are and incisive and direct textual explanations of the distinguishing features are generated. A further study analyzes an interpretable mapping between the semantic space of the text and the space of the image.

Head pose estimation is not only a crucial challenge for many real-world applications, such as driver attention detection analysis, but it represents an interesting strategy to support biometric frameworks as well. This paper aims to estimate head pose from a single image by applying notions of network curvature. In the real world, many complex networks have groups of nodes that are well connected to each other with significant functional roles. Similarly, the interactions of facial landmarks can be represented as complex dynamic systems modeled by weighted graphs. The functionality of such a system is therefore intrinsically linked to the topology and geometry of the underlying graph. In this work, using the geometric notion of Ollivier-Ricci curvature (ORC) on weighted graphs as input to the XGBoost regression model, we show that the intrinsic geometric basis of ORC offers a natural approach to discovering underlying common structure within a pool of poses. Experiments on the BIWI, AFLW2000 and Pointing '04 datasets show that the ORC_XGB method performs well compared to state-of-the-art methods, both landmark-based and image-only.

Pepper is a humanoid robot capable of expressing body language, perceiving, and interacting with its surrounding environment, thanks to a wide set of sensors and actuators and exposing capabilities and high-level interfaces for natural interaction with humans. In this article, we present the development of VPepper, the Pepper virtual replica, by describing experiences focused on the interaction of the digital twin with the replicas of the smart objects in a smart home. Pepper robot has been featured with arms and hands, but its motors and actuators cannot support intensive experimental sessions and training procedures to learn how safely touch objects. Here, digital twin metaphor plays a crucial role. By a virtual and reliable replica of the robot, machine learning procedures can be seamlessly moved to/from the digital twin with a significant speedup and preventing the physical robot from deterioration. As a practical application, the reported case study is inspired to ambient-assisted living in elderly assistance. The experience, as well as the entire design and development process, has revealed VPepper and the smart environment to offer interesting opportunities for the physical accuracy of the simulation and for the availability of machine learning instruments that may be converted and adopted for real settings. A final empirical evaluation, performed involving 25 volunteer caregivers, confirms the perceived value and the potential usefulness of the system.

Social robots adopt an emotional touch to interact with users inducing and transmitting humanlike emotions. Natural interaction with humans needs to be in real time and well grounded on the full availability of information on the environment. These robots base their way of communicating on direct interaction (touch, listening, view), supported by a range of sensors on the surrounding environment that provide a radially central and partial knowledge on it. Over the past few years, social robots have been demonstrated to implement different features, going from biometric applications to the fusion of machine learning environmental information collected on the edge. This article aims at describing the experiences performed and still ongoing and characterizes a simulation environment developed for the social robot Pepper that aims to foresee the new scenarios and benefits that tactile connectivity will enable.