The development of driver monitoring systems (DMS) has emerged as a pivotal advancement in automotive safety to monitor driver behaviors and prevent road accidents. Driver emotions are one of the critical factors that affect driving safety. While numerous studies have explored facial macro-expressions (FMaEs) of drivers, these facial expressions can be consciously controlled, allowing individuals to conceal their true negative emotions. However, inaccurate capturing of true emotions can lead to severe accidents. In this paper, we focus on facial micro-expressions (FMEs), which are brief and involuntary facial movements that reveal deeper insights into genuine emotions. Thereby, they provide a more accurate measure of a driver's emotional condition, and are crucial for assessing driver readiness and safety. To facilitate FME studies of drivers, we analyzed driver videos from an affective driver-pedestrian interaction experiment, providing corresponding facial action units and FME annotations, and built a real driver FME dataset. By analyzing the driver data, we found that there are more FMEs when drivers respond to negative stimuli compared with positive stimuli, and there are more FMaEs when drivers respond to positive stimuli compared with negative stimuli. To protect driver identities while maintaining the integrity of the facial movements, we released a synthesized driver FME dataset. Furthermore, we benchmarked deep learning-based architectures on the real and synthetic driver FME datasets, uncovering the challenges inherent in recognizing FMEs in real-world driving scenarios.

Facial micro-expressions (MEs) are involuntary movements of the face that occur spontaneously when a person experiences an emotion but attempts to suppress or repress the facial expression, typically found in a high-stakes environment. In recent years, substantial advancements have been made in the areas of ME recognition, spotting, and generation. However, conventional approaches that treat spotting and recognition as separate tasks are suboptimal, particularly for analyzing long-duration videos in realistic settings. Concurrently, the emergence of multimodal large language models (MLLMs) and large vision-language models (LVLMs) offers promising new avenues for enhancing ME analysis through their powerful multimodal reasoning capabilities. The ME grand challenge (MEGC) 2025 introduces two tasks that reflect these evolving research directions: (1) ME spot-then-recognize (ME-STR), which integrates ME spotting and subsequent recognition in a unified sequential pipeline; and (2) ME visual question answering (ME-VQA), which explores ME understanding through visual question answering, leveraging MLLMs or LVLMs to address diverse question types related to MEs. All participating algorithms are required to run on this test set and submit their results on a leaderboard. More details are available at https://megc2025.github.io.

Recently attention-based networks have been successful for image restoration tasks. However, existing methods are either computationally expensive or have limited receptive fields, adding constraints to the model. They are also less resilient in spatial and contextual aspects and lack pixel-to-pixel correspondence, which may degrade feature representations. In this paper, we propose a novel and computationally efficient architecture Single Stage Adaptive Multi-Attention Network (SSAMAN) for image restoration tasks, particularly for image denoising and image deblurring. SSAMAN efficiently addresses computational challenges and expands receptive fields, enhancing robustness in spatial and contextual feature representation. Its Adaptive Multi-Attention Module (AMAM), which consists of Adaptive Pixel Attention Branch (APAB) and an Adaptive Channel Attention Branch (ACAB), uniquely integrates channel and pixel-wise dimensions, significantly improving sensitivity to edges, shapes, and textures. We perform extensive experiments and ablation studies to validate the performance of SSAMAN. Our model shows state-of-the-art results on various benchmarks, for example, on image denoising tasks, SSAMAN achieves a notable 40.08 dB PSNR on SIDD dataset, outperforming Restormer by 0.06 dB PSNR, with 41.02% less computational cost, and achieves a 40.05 dB PSNR on the DND dataset. For image deblurring, SSAMAN achieves 33.53 dB PSNR on GoPro dataset. Code and models are available at Github.

Facial micro-expressions (MEs) refer to brief spontaneous facial movements that can reveal a person's genuine emotion. They are valuable in lie detection, criminal analysis, and other areas. While deep learning-based ME recognition (MER) methods achieved impressive success, these methods typically require pre-processing using conventional optical flow-based methods to extract facial motions as inputs. To overcome this limitation, we proposed a novel MER framework using self-supervised learning to extract facial motion for ME (SelfME). To the best of our knowledge, this is the first work using an automatically self-learned motion technique for MER. However, the self-supervised motion learning method might suffer from ignoring symmetrical facial actions on the left and right sides of faces when extracting fine features. To address this issue, we developed a symmetric contrastive vision transformer (SCViT) to constrain the learning of similar facial action features for the left and right parts of faces. Experiments were conducted on two benchmark datasets showing that our method achieved state-of-the-art performance, and ablation studies demonstrated the effectiveness of our method.

Facial micro-expression (ME) refers to a brief spontaneous facial movement that can reveal the genuine emotion of a person. The absence of data is a major problem for ME. Thankfully, generative deep neural network models can aid in producing desired samples. In this work, we proposed a deep learning based adaptive dual motion model (ADMM) for generating facial ME samples. A dual motion extraction (DME) module extracts robust motions from two modalities: original color images and edge-based grayscale images, with dual streams. Using edge-based grayscale images can help the method focus on learning subtle movements by eliminating the influences of noises and illumination variants. The motions extracted by the dual streams are fed into an adaptive motion fusion (AMF) module for combing the motions adaptively to generate the dense motion. Our method was trained on the CASME II, SMIC, and SAMM datasets. The evaluation and analysis of the results demonstrated the effectiveness of our method.

Facial micro-expression (FME) refers to a brief spontaneous facial movement that can reveal a person's genius emotion. One challenge in facial micro-expression is the lack of data. Fortunately, generative deep neural network models can assist in the creation of desired images. However, the issues for micro-expressions are the facial variations are too subtle to capture, and the limited training data may make feature extraction difficult. To address these issues, we developed a deep motion retargeting and transfer learning based facial micro-expression generation model (DMT-FMEG). First, to capture subtle variations, we employed a deep motion retargeting (DMR) network that can learn keypoints in an unsupervised manner, estimate motions, and generate desired images. Second, to enhance the feature extraction ability, we applied deep transfer learning (DTL) by borrowing knowledge from macro-expression images. We evaluated our method on three datasets, CASME II, SMIC, and SAMM, and found that it showed satisfactory results on all of them. With the effectiveness of the method, we won the second place in the generation task of the FME 2021 challenge.

Coronavirus 2019 has made a significant impact on the world. One effective strategy to prevent infection for people is to wear masks in public places. Certain public service providers require clients to use their services only if they properly wear masks. There are, however, only a few research studies on automatic face mask detection. In this paper, we proposed RetinaFaceMask, the first high-performance single stage face mask detector. First, to solve the issue that existing studies did not distinguish between correct and incorrect mask wearing states, we established a new dataset containing these annotations. Second, we proposed a context attention module to focus on learning discriminated features associated with face mask wearing states. Third, we transferred the knowledge from the face detection task, inspired by how humans improve their ability via learning from similar tasks. Ablation studies showed the advantages of the proposed model. Experimental findings on both the public and new datasets demonstrated the state-of-the-art performance of our model.

Travel time estimation plays a core role in intelligent traffic systems, as people or smart vehicles can choose a better travel route in advance, while local authorities can make urban traffic planning based on it. The paper proposes a dual traffic network topology coupled with improved delay models to estimate travel time in urban traffic system. The structure of the system is demonstrated by the dual traffic network (DTN) topology. It hires nodes to representjoint points of links and intersections; it also has two kinds of edges-links and turning cases of intersections. The BPR model is improved by concerning the influence of pedestrians and the lane width, and the HCM model is improved by adding travel time at intersections and considering three different turning cases to match with the DTN topology. The proposed methodology is evaluated by a comparison and an experiment in Changzhou, China. The error is greatly reduced, even though the degree of saturation exceeds 0.72. This proposed estimation methodology can improve the estimation accuracy significantly, especially in high degree of saturation cases.

People watching smartphones while walking causes a significant impact to their safety. Pedestrians staring at smartphone screens while walking along the sidewalk are generally more at risk than other pedestrians not engaged in smartphone usage. In this study, the authors propose Safe Walking , an Android smartphone‐based system that detects the walking behaviour of pedestrians by leveraging the sensors and front camera on smartphones, improving the safety of pedestrians staring at smartphone screens. More specifically, Safe Walking first exploits a pedestrian speed calculation algorithm by sampling acceleration data via the accelerometer and calculating gravity components via the gravity sensor. Then, this system utilises a greyscale image detection algorithm to detect the face and eye movement modes based on OpenCV4Android to determine if pedestrians are staring at the screens. Finally, Safe Walking generates a vibration by a vibrator on smartphones to alert pedestrians to pay attention to road conditions. The authors implemented Safe Walking on an Android smartphone and evaluated pedestrian walking speed, the accuracy of eye movement, and system performance. The results show that Safe Walking can prevent the potential danger for pedestrians staring at smartphone screens with a true positive rate of .