In the realm of manufacturing processes, equipment failures can result in substantial financial losses and pose significant safety hazards. Consequently, prior research has primarily been focused on preemptively detecting anomalies before they manifest. However, within industrial contexts, the precise interpretation of predictive outcomes holds paramount importance. This has spurred the development of research in Explainable Artificial Intelligence (XAI) to elucidate the inner workings of predictive models. Previous studies have endeavored to furnish explanations for anomaly detection within these models. Nonetheless, rectifying these anomalies typically necessitates the expertise of seasoned professionals. Therefore, our study extends beyond the mere identification of anomaly causes; we also ascertain the specific adjustments required to normalize these deviations. In this paper, we present novel research avenues and introduce three methods to tackle this challenge. Each method has exhibited a remarkable success rate in normalizing detected errors, scoring 97.30%, 97.30%, and 100.0%, respectively. This research not only contributes to the field of anomaly detection but also amplifies the practical applicability of these models in industrial environments. It furnishes actionable insights for error correction, thereby enhancing their utility and efficacy in real-world scenarios.

Recently, technologies monitoring users’ physiological signals in consumer electronics such as smartphones or kiosks with cameras and displays are gaining attention for their potential role in diverse services. While many of these technologies focus on photoplethysmography for the measurement of blood flow changes, respiratory measurement is also essential for assessing an individual’s health status. Previous studies have proposed thermal camera-based and body movement-based respiratory measurement methods. In this paper, we adopt an approach to extract respiratory signals from RGB face videos using photoplethysmography. Prior research shows that photoplethysmography can measure respiratory signals, due to its correlation with cardiac activity, by setting arterial vessel regions as areas of interest for respiratory measurement. However, this correlation does not directly reflect real-time respiratory components in photoplethysmography. Our new approach measures the respiratory rate by capturing changes in skin brightness from motion artifacts. We utilize these brightness factors, including facial movement, for respiratory signal measurement. We applied the wavelet transform and smoothing filters to remove other unrelated motion artifacts. In order to validate our method, we built a dataset of respiratory rate measurements from 20 individuals using an RGB camera in a facial movement-aware environment. Our approach demonstrated a similar performance level to the reference signal obtained with a contact-based respiratory belt, with a correlation above 0.9 and an MAE within 1 bpm. Moreover, our approach offers advantages for real-time measurements, excluding complex computational processes for measuring optical flow caused by the movement of the chest due to respiration.

Remote vital signal estimation has been researched for several years. There are numerous studies on rPPG, which utilizes cameras to detect cardiovascular activity. Most of the research has concentrated on obtaining rPPG from a complete video. However, excessive movement or changes in lighting can cause noise, and it will inevitably lead to a reduction in the quality of the obtained signal. Moreover, since rPPG measures minor changes that occur in the blood flow of an image due to variations in heart rate, it becomes challenging to capture in a noisy image, as the impact of noise is larger than the change caused by the heart rate. Using such segments in a video can cause a decrease in overall performance, but it can only be remedied through data pre-processing. In this study, we propose a screening technique that removes excessively noisy video segments as input and only uses signals obtained from reliable segments. Using this method, we were able to boost the performance of the current rPPG algorithm from 50.43% to 62.27% based on PTE6. Our screening technique can be easily applied to any existing rPPG prediction model and it can improve the reliability of the output in all cases.

Despite major breakthroughs in facial recognition technology, problems with bias and a lack of diversity still plague face recognition systems today. To address these issues, we created synthetic face data using a diffusion-based generative model and fine-tuned already-high-performing models. To achieve a more balanced overall performance across various races, the synthetic dataset was created by following the dual-condition face generator (DCFace) resolution and using race-varied data from BUPT-BalancedFace as well as FairFace. To verify the proposed method, we fine-tuned a pre-trained improved residual networks (IResnet)-100 model with additive angular margin (ArcFace) loss using the synthetic dataset. The results show that the racial gap in performance is reduced from 0.0107 to 0.0098 in standard deviation terms, while the overall accuracy increases from 96.125% to 96.1625%. The improved racial balance and diversity in the synthetic dataset led to an improvement in model fairness, demonstrating that this resource could facilitate more equitable face recognition systems. This method provides a low-cost way to address data diversity challenges and help make face recognition more accurate across different demographic groups. The results of the study highlighted that more advanced synthesized datasets, created through diffusion-based models, can also result in increased facial recognition accuracy with greater fairness, emphasizing that these should not be ignored by developers aiming to create artificial intelligence (AI) systems.

Nervousness is a complex emotional state characterized by high arousal and ambiguous valence, often triggered in high-stress environments. This study presents a mathematical and computational framework for defining and classifying nervousness using facial expression data projected onto a valence–arousal (V–A) space. A statistical approach employing the Minimum Covariance Determinant (MCD) estimator is used to construct 90% and 99% confidence ellipses for nervous and non-nervous states, respectively, using Mahalanobis distance. These ellipses form the basis for binary labeling of the AffectNet dataset. We apply a deep learning model trained via knowledge distillation, with EmoNet as the teacher and MobileNetV2 as the student, to efficiently classify nervousness. The experimental results on the AffectNet dataset show that our proposed method achieves a classification accuracy of 81.08%, improving over the baseline by approximately 6%. These results are obtained by refining the valence–arousal distributions and applying knowledge distillation from EmoNet to MobileNetV2. We use accuracy and F1-score as evaluation metrics to validate the performance. Furthermore, we perform a qualitative analysis using action unit (AU) activation graphs to provide deeper insight into nervous facial expressions. The proposed method demonstrates how mathematical tools and deep learning can be integrated for robust affective state modeling.

Facial expression recognition is a pivotal area in computer vision, traditionally focusing on categorical labels such as ‘Happy’, and ‘Sad’. Recent advancements have transitioned towards using the continuous indicators valence and arousal, reflecting the complicated nature of human emotions. This study introduces a method using semi-supervised learning to generate valence and arousal labels for existing categorical datasets, addressing challenges like racial bias. We propose a pseudo-label refinement framework, VAP-Refine (Valence-Arousal Pseudo-label Refinement), which enhances facial expression recognition by combining teacher model predictions and category-level statistics. These predicted labels are adjusted using ground truth category information and combined with actual category labels to train a more robust facial expression recognition model. Our approach improved accuracy from 69.3% to 72.26% and from 62.94% to 67.24% across two datasets. Fine-tuning with a teacher model predicting valence and arousal achieved 75.82% and 91.58% accuracy, with an F1-score of 0.9147, despite data imbalances. These results highlight the potential of semi-supervised learning to enhance facial expression recognition by incorporating continuous emotional indicators, improving model performance and contributing to more accurate affective computing applications. Furthermore, the proposed framework consistently improved performance across various backbone architectures, including ResNet50, SHViT, and DDAMFN++, highlighting its generalizability and versatility.