MienCap: Realtime Performance-Based Facial Animation with Live Mood Dynamics

Our purpose is to improve performance-based animation which can drive believable 3D stylized characters that are truly perceptual. By combining traditional blendshape animation techniques with multiple machine learning models, we present both non-real time and real time solutions which drive character expressions in a geometrically consistent and perceptually valid way. For the non-real time system, we propose a 3D emotion transfer network makes use of a 2D human image to generate a stylized 3D rig parameters. For the real time system, we propose a blendshape adaption network which generates the character rig parameter motions with geometric consistency and temporally stability. We demonstrate the effectiveness of our system by comparing to a commercial product Faceware. Results reveal that ratings of the recognition, intensity, and attractiveness of expressions depicted for animated characters via our systems are statistically higher than Faceware. Our results may be implemented into the animation pipeline, and provide animators with a system for creating the expressions they wish to use more quickly and accurately.

Touch Speaks, Sound Feels: A Multimodal Approach to Affective and Social Touch from Robots to Humans

Affective tactile interaction constitutes a fundamental component of human communication. In natural human-human encounters, touch is seldom experienced in isolation; rather, it is inherently multisensory. Individuals not only perceive the physical sensation of touch but also register the accompanying auditory cues generated through contact. The integration of haptic and auditory information forms a rich and nuanced channel for emotional expression. While extensive research has examined how robots convey emotions through facial expressions and speech, their capacity to communicate social gestures and emotions via touch remains largely underexplored. To address this gap, we developed a multimodal interaction system incorporating a 5*5 grid of 25 vibration motors synchronized with audio playback, enabling robots to deliver combined haptic-audio stimuli. In an experiment involving 32 Chinese participants, ten emotions and six social gestures were presented through vibration, sound, or their combination. Participants rated each stimulus on arousal and valence scales. The results revealed that (1) the combined haptic-audio modality significantly enhanced decoding accuracy compared to single modalities; (2) each individual channel-vibration or sound-effectively supported certain emotions recognition, with distinct advantages depending on the emotional expression; and (3) gestures alone were generally insufficient for conveying clearly distinguishable emotions. These findings underscore the importance of multisensory integration in affective human-robot interaction and highlight the complementary roles of haptic and auditory cues in enhancing emotional communication.

FG 2025 TrustFAA: the First Workshop on Towards Trustworthy Facial Affect Analysis: Advancing Insights of Fairness, Explainability, and Safety (TrustFAA)

With the increasing prevalence and deployment of Emotion AI-powered facial affect analysis (FAA) tools, concerns about the trustworthiness of these systems have become more prominent. This first workshop on "Towards Trustworthy Facial Affect Analysis: Advancing Insights of Fairness, Explainability, and Safety (TrustFAA)" aims to bring together researchers who are investigating different challenges in relation to trustworthiness-such as interpretability, uncertainty, biases, and privacy-across various facial affect analysis tasks, including macro/ micro-expression recognition, facial action unit detection, other corresponding applications such as pain and depression detection, as well as human-robot interaction and collaboration. In alignment with FG2025's emphasis on ethics, as demonstrated by the inclusion of an Ethical Impact Statement requirement for this year's submissions, this workshop supports FG2025's efforts by encouraging research, discussion and dialogue on trustworthy FAA.

Attributes Shape the Embedding Space of Face Recognition Models

Face Recognition (FR) tasks have made significant progress with the advent of Deep Neural Networks, particularly through margin-based triplet losses that embed facial images into high-dimensional feature spaces. During training, these contrastive losses focus exclusively on identity information as labels. However, we observe a multiscale geometric structure emerging in the embedding space, influenced by interpretable facial (e.g., hair color) and image attributes (e.g., contrast). We propose a geometric approach to describe the dependence or invariance of FR models to these attributes and introduce a physics-inspired alignment metric. We evaluate the proposed metric on controlled, simplified models and widely used FR models fine-tuned with synthetic data for targeted attribute augmentation. Our findings reveal that the models exhibit varying degrees of invariance across different attributes, providing insight into their strengths and weaknesses and enabling deeper interpretability. Code available here: https://github.com/mantonios107/attrs-fr-embs}{https://github.com/mantonios107/attrs-fr-embs

OPEN: A Benchmark Dataset and Baseline for Older Adult Patient Engagement Recognition in Virtual Rehabilitation Learning Environments

Engagement in virtual learning is essential for participant satisfaction, performance, and adherence, particularly in online education and virtual rehabilitation, where interactive communication plays a key role. Yet, accurately measuring engagement in virtual group settings remains a challenge. There is increasing interest in using artificial intelligence (AI) for large-scale, real-world, automated engagement recognition. While engagement has been widely studied in younger academic populations, research and datasets focused on older adults in virtual and telehealth learning settings remain limited. Existing methods often neglect contextual relevance and the longitudinal nature of engagement across sessions. This paper introduces OPEN (Older adult Patient ENgagement), a novel dataset supporting AI-driven engagement recognition. It was collected from eleven older adults participating in weekly virtual group learning sessions over six weeks as part of cardiac rehabilitation, producing over 35 hours of data, making it the largest dataset of its kind. To protect privacy, raw video is withheld; instead, the released data include facial, hand, and body joint landmarks, along with affective and behavioral features extracted from video. Annotations include binary engagement states, affective and behavioral labels, and context-type indicators, such as whether the instructor addressed the group or an individual. The dataset offers versions with 5-, 10-, 30-second, and variable-length samples. To demonstrate utility, multiple machine learning and deep learning models were trained, achieving engagement recognition accuracy of up to 81 percent. OPEN provides a scalable foundation for personalized engagement modeling in aging populations and contributes to broader engagement recognition research.

A Deep Learning Approach for Facial Attribute Manipulation and Reconstruction in Surveillance and Reconnaissance

Surveillance systems play a critical role in security and reconnaissance, but their performance is often compromised by low-quality images and videos, leading to reduced accuracy in face recognition. Additionally, existing AI-based facial analysis models suffer from biases related to skin tone variations and partially occluded faces, further limiting their effectiveness in diverse real-world scenarios. These challenges are the results of data limitations and imbalances, where available training datasets lack sufficient diversity, resulting in unfair and unreliable facial recognition performance. To address these issues, we propose a data-driven platform that enhances surveillance capabilities by generating synthetic training data tailored to compensate for dataset biases. Our approach leverages deep learning-based facial attribute manipulation and reconstruction using autoencoders and Generative Adversarial Networks (GANs) to create diverse and high-quality facial datasets. Additionally, our system integrates an image enhancement module, improving the clarity of low-resolution or occluded faces in surveillance footage. We evaluate our approach using the CelebA dataset, demonstrating that the proposed platform enhances both training data diversity and model fairness. This work contributes to reducing bias in AI-based facial analysis and improving surveillance accuracy in challenging environments, leading to fairer and more reliable security applications.

SocialDF: Benchmark Dataset and Detection Model for Mitigating Harmful Deepfake Content on Social Media Platforms

The rapid advancement of deep generative models has significantly improved the realism of synthetic media, presenting both opportunities and security challenges. While deepfake technology has valuable applications in entertainment and accessibility, it has emerged as a potent vector for misinformation campaigns, particularly on social media. Existing detection frameworks struggle to distinguish between benign and adversarially generated deepfakes engineered to manipulate public perception. To address this challenge, we introduce SocialDF, a curated dataset reflecting real-world deepfake challenges on social media platforms. This dataset encompasses high-fidelity deepfakes sourced from various online ecosystems, ensuring broad coverage of manipulative techniques. We propose a novel LLM-based multi-factor detection approach that combines facial recognition, automated speech transcription, and a multi-agent LLM pipeline to cross-verify audio-visual cues. Our methodology emphasizes robust, multi-modal verification techniques that incorporate linguistic, behavioral, and contextual analysis to effectively discern synthetic media from authentic content.

Facial Recognition Leveraging Generative Adversarial Networks

Face recognition performance based on deep learning heavily relies on large-scale training data, which is often difficult to acquire in practical applications. To address this challenge, this paper proposes a GAN-based data augmentation method with three key contributions: (1) a residual-embedded generator to alleviate gradient vanishing/exploding problems, (2) an Inception ResNet-V1 based FaceNet discriminator for improved adversarial training, and (3) an end-to-end framework that jointly optimizes data generation and recognition performance. Experimental results demonstrate that our approach achieves stable training dynamics and significantly improves face recognition accuracy by 12.7% on the LFW benchmark compared to baseline methods, while maintaining good generalization capability with limited training samples.

Accuracy and Fairness of Facial Recognition Technology in Low-Quality Police Images: An Experiment With Synthetic Faces

Facial recognition technology (FRT) is increasingly used in criminal investigations, yet most evaluations of its accuracy rely on high-quality images, unlike those often encountered by law enforcement. This study examines how five common forms of image degradation--contrast, brightness, motion blur, pose shift, and resolution--affect FRT accuracy and fairness across demographic groups. Using synthetic faces generated by StyleGAN3 and labeled with FairFace, we simulate degraded images and evaluate performance using Deepface with ArcFace loss in 1:n identification tasks. We perform an experiment and find that false positive rates peak near baseline image quality, while false negatives increase as degradation intensifies--especially with blur and low resolution. Error rates are consistently higher for women and Black individuals, with Black females most affected. These disparities raise concerns about fairness and reliability when FRT is used in real-world investigative contexts. Nevertheless, even under the most challenging conditions and for the most affected subgroups, FRT accuracy remains substantially higher than that of many traditional forensic methods. This suggests that, if appropriately validated and regulated, FRT should be considered a valuable investigative tool. However, algorithmic accuracy alone is not sufficient: we must also evaluate how FRT is used in practice, including user-driven data manipulation. Such cases underscore the need for transparency and oversight in FRT deployment to ensure both fairness and forensic validity.

2D-3D Attention and Entropy for Pose Robust 2D Facial Recognition

Despite recent advances in facial recognition, there remains a fundamental issue concerning degradations in performance due to substantial perspective (pose) differences between enrollment and query (probe) imagery. Therefore, we propose a novel domain adaptive framework to facilitate improved performances across large discrepancies in pose by enabling image-based (2D) representations to infer properties of inherently pose invariant point cloud (3D) representations. Specifically, our proposed framework achieves better pose invariance by using (1) a shared (joint) attention mapping to emphasize common patterns that are most correlated between 2D facial images and 3D facial data and (2) a joint entropy regularizing loss to promote better consistency$\unicode{x2014}$enhancing correlations among the intersecting 2D and 3D representations$\unicode{x2014}$by leveraging both attention maps. This framework is evaluated on FaceScape and ARL-VTF datasets, where it outperforms competitive methods by achieving profile (90$\unicode{x00b0}$$\unicode{x002b}$) TAR @ 1$\unicode{x0025}$ FAR improvements of at least 7.1$\unicode{x0025}$ and 1.57$\unicode{x0025}$, respectively.