Experimenting with Affective Computing Models in Video Interviews with Spanish-speaking Older Adults

Understanding emotional signals in older adults is crucial for designing virtual assistants that support their well-being. However, existing affective computing models often face significant limitations: (1) limited availability of datasets representing older adults, especially in non-English-speaking populations, and (2) poor generalization of models trained on younger or homogeneous demographics. To address these gaps, this study evaluates state-of-the-art affective computing models -- including facial expression recognition, text sentiment analysis, and smile detection -- using videos of older adults interacting with either a person or a virtual avatar. As part of this effort, we introduce a novel dataset featuring Spanish-speaking older adults engaged in human-to-human video interviews. Through three comprehensive analyses, we investigate (1) the alignment between human-annotated labels and automatic model outputs, (2) the relationships between model outputs across different modalities, and (3) individual variations in emotional signals. Using both the Wizard of Oz (WoZ) dataset and our newly collected dataset, we uncover limited agreement between human annotations and model predictions, weak consistency across modalities, and significant variability among individuals. These findings highlight the shortcomings of generalized emotion perception models and emphasize the need of incorporating personal variability and cultural nuances into future systems.

OODFace: Benchmarking Robustness of Face Recognition under Common Corruptions and Appearance Variations

With the rise of deep learning, facial recognition technology has seen extensive research and rapid development. Although facial recognition is considered a mature technology, we find that existing open-source models and commercial algorithms lack robustness in certain real-world Out-of-Distribution (OOD) scenarios, raising concerns about the reliability of these systems. In this paper, we introduce OODFace, which explores the OOD challenges faced by facial recognition models from two perspectives: common corruptions and appearance variations. We systematically design 30 OOD scenarios across 9 major categories tailored for facial recognition. By simulating these challenges on public datasets, we establish three robustness benchmarks: LFW-C/V, CFP-FP-C/V, and YTF-C/V. We then conduct extensive experiments on 19 different facial recognition models and 3 commercial APIs, along with extended experiments on face masks, Vision-Language Models (VLMs), and defense strategies to assess their robustness. Based on the results, we draw several key insights, highlighting the vulnerability of facial recognition systems to OOD data and suggesting possible solutions. Additionally, we offer a unified toolkit that includes all corruption and variation types, easily extendable to other datasets. We hope that our benchmarks and findings can provide guidance for future improvements in facial recognition model robustness.

Multi-face emotion detection for effective Human-Robot Interaction

The integration of dialogue interfaces in mobile devices has become ubiquitous, providing a wide array of services. As technology progresses, humanoid robots designed with human-like features to interact effectively with people are gaining prominence, and the use of advanced human-robot dialogue interfaces is continually expanding. In this context, emotion recognition plays a crucial role in enhancing human-robot interaction by enabling robots to understand human intentions. This research proposes a facial emotion detection interface integrated into a mobile humanoid robot, capable of displaying real-time emotions from multiple individuals on a user interface. To this end, various deep neural network models for facial expression recognition were developed and evaluated under consistent computer-based conditions, yielding promising results. Afterwards, a trade-off between accuracy and memory footprint was carefully considered to effectively implement this application on a mobile humanoid robot.

Two-Stream Spatial-Temporal Transformer Framework for Person Identification via Natural Conversational Keypoints

In the age of AI-driven generative technologies, traditional biometric recognition systems face unprecedented challenges, particularly from sophisticated deepfake and face reenactment techniques. In this study, we propose a Two-Stream Spatial-Temporal Transformer Framework for person identification using upper body keypoints visible during online conversations, which we term conversational keypoints. Our framework processes both spatial relationships between keypoints and their temporal evolution through two specialized branches: a Spatial Transformer (STR) that learns distinctive structural patterns in keypoint configurations, and a Temporal Transformer (TTR) that captures sequential motion patterns. Using the state-of-the-art Sapiens pose estimator, we extract 133 keypoints (based on COCO-WholeBody format) representing facial features, head pose, and hand positions. The framework was evaluated on a dataset of 114 individuals engaged in natural conversations, achieving recognition accuracies of 80.12% for the spatial stream, 63.61% for the temporal stream. We then explored two fusion strategies: a shared loss function approach achieving 82.22% accuracy, and a feature-level fusion method that concatenates feature maps from both streams, significantly improving performance to 94.86%. By jointly modeling both static anatomical relationships and dynamic movement patterns, our approach learns comprehensive identity signatures that are more robust to spoofing than traditional appearance-based methods.

NullSwap: Proactive Identity Cloaking Against Deepfake Face Swapping

Suffering from performance bottlenecks in passively detecting high-quality Deepfake images due to the advancement of generative models, proactive perturbations offer a promising approach to disabling Deepfake manipulations by inserting signals into benign images. However, existing proactive perturbation approaches remain unsatisfactory in several aspects: 1) visual degradation due to direct element-wise addition; 2) limited effectiveness against face swapping manipulation; 3) unavoidable reliance on white- and grey-box settings to involve generative models during training. In this study, we analyze the essence of Deepfake face swapping and argue the necessity of protecting source identities rather than target images, and we propose NullSwap, a novel proactive defense approach that cloaks source image identities and nullifies face swapping under a pure black-box scenario. We design an Identity Extraction module to obtain facial identity features from the source image, while a Perturbation Block is then devised to generate identity-guided perturbations accordingly. Meanwhile, a Feature Block extracts shallow-level image features, which are then fused with the perturbation in the Cloaking Block for image reconstruction. Furthermore, to ensure adaptability across different identity extractors in face swapping algorithms, we propose Dynamic Loss Weighting to adaptively balance identity losses. Experiments demonstrate the outstanding ability of our approach to fool various identity recognition models, outperforming state-of-the-art proactive perturbations in preventing face swapping models from generating images with correct source identities.

Semantic Data Augmentation for Long-tailed Facial Expression Recognition

Facial Expression Recognition has a wide application prospect in social robotics, health care, driver fatigue monitoring, and many other practical scenarios. Automatic recognition of facial expressions has been extensively studied by the Computer Vision research society. But Facial Expression Recognition in real-world is still a challenging task, partially due to the long-tailed distribution of the dataset. Many recent studies use data augmentation for Long-Tailed Recognition tasks. In this paper, we propose a novel semantic augmentation method. By introducing randomness into the encoding of the source data in the latent space of VAE-GAN, new samples are generated. Then, for facial expression recognition in RAF-DB dataset, we use our augmentation method to balance the long-tailed distribution. Our method can be used in not only FER tasks, but also more diverse data-hungry scenarios.

A Multimodal Emotion Recognition System: Integrating Facial Expressions, Body Movement, Speech, and Spoken Language

Traditional psychological evaluations rely heavily on human observation and interpretation, which are prone to subjectivity, bias, fatigue, and inconsistency. To address these limitations, this work presents a multimodal emotion recognition system that provides a standardised, objective, and data-driven tool to support evaluators, such as psychologists, psychiatrists, and clinicians. The system integrates recognition of facial expressions, speech, spoken language, and body movement analysis to capture subtle emotional cues that are often overlooked in human evaluations. By combining these modalities, the system provides more robust and comprehensive emotional state assessment, reducing the risk of mis- and overdiagnosis. Preliminary testing in a simulated real-world condition demonstrates the system's potential to provide reliable emotional insights to improve the diagnostic accuracy. This work highlights the promise of automated multimodal analysis as a valuable complement to traditional psychological evaluation practices, with applications in clinical and therapeutic settings.

Driver Assistance System Based on Multimodal Data Hazard Detection

Autonomous driving technology has advanced significantly, yet detecting driving anomalies remains a major challenge due to the long-tailed distribution of driving events. Existing methods primarily rely on single-modal road condition video data, which limits their ability to capture rare and unpredictable driving incidents. This paper proposes a multimodal driver assistance detection system that integrates road condition video, driver facial video, and audio data to enhance incident recognition accuracy. Our model employs an attention-based intermediate fusion strategy, enabling end-to-end learning without separate feature extraction. To support this approach, we develop a new three-modality dataset using a driving simulator. Experimental results demonstrate that our method effectively captures cross-modal correlations, reducing misjudgments and improving driving safety.

Interpretable Face Anti-Spoofing: Enhancing Generalization with Multimodal Large Language Models

Face Anti-Spoofing (FAS) is essential for ensuring the security and reliability of facial recognition systems. Most existing FAS methods are formulated as binary classification tasks, providing confidence scores without interpretation. They exhibit limited generalization in out-of-domain scenarios, such as new environments or unseen spoofing types. In this work, we introduce a multimodal large language model (MLLM) framework for FAS, termed Interpretable Face Anti-Spoofing (I-FAS), which transforms the FAS task into an interpretable visual question answering (VQA) paradigm. Specifically, we propose a Spoof-aware Captioning and Filtering (SCF) strategy to generate high-quality captions for FAS images, enriching the model's supervision with natural language interpretations. To mitigate the impact of noisy captions during training, we develop a Lopsided Language Model (L-LM) loss function that separates loss calculations for judgment and interpretation, prioritizing the optimization of the former. Furthermore, to enhance the model's perception of global visual features, we design a Globally Aware Connector (GAC) to align multi-level visual representations with the language model. Extensive experiments on standard and newly devised One to Eleven cross-domain benchmarks, comprising 12 public datasets, demonstrate that our method significantly outperforms state-of-the-art methods.

DIPSER: A Dataset for In-Person Student1 Engagement Recognition in the Wild

In this paper, a novel dataset is introduced, designed to assess student attention within in-person classroom settings. This dataset encompasses RGB camera data, featuring multiple cameras per student to capture both posture and facial expressions, in addition to smartwatch sensor data for each individual. This dataset allows machine learning algorithms to be trained to predict attention and correlate it with emotion. A comprehensive suite of attention and emotion labels for each student is provided, generated through self-reporting as well as evaluations by four different experts. Our dataset uniquely combines facial and environmental camera data, smartwatch metrics, and includes underrepresented ethnicities in similar datasets, all within in-the-wild, in-person settings, making it the most comprehensive dataset of its kind currently available. The dataset presented offers an extensive and diverse collection of data pertaining to student interactions across different educational contexts, augmented with additional metadata from other tools. This initiative addresses existing deficiencies by offering a valuable resource for the analysis of student attention and emotion in face-to-face lessons.