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.

Facial micro-expressions (MEs) are involuntary spontaneous movements of the face that typically appear in high-stakes situations where a person attempts to conceal a certain emotion from being known. A decade after the inception of the widely used CASME II and SMIC datasets, research in computational analysis of MEs has now advanced toward new pathways, exploring problems crucial to model generalization and real-world practicality. It is often challenging to design robust algorithms or models for spotting micro-expressions due to the high variability across diverse cultural backgrounds. Also, treating spotting and recognition as separate tasks is undesirable when handling long-spanning videos under realistic settings. This Grand Challenge comprises two distinct tracks: the Cross-Cultural Spotting (CCS) track, and the Spot-Then-Recognize (STR) track. All participating solutions submitted their results to a leaderboard, and several submissions performed well surpassing their respective baseline results. More details are available at: https://megc2024.github.io.

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. Unfortunately, the small sample problem severely limits the automation of ME analysis. Furthermore, due to the weak and transient nature of MEs, it is difficult for models to distinguish it from other types of facial actions. Therefore, ME in long videos is a challenging task, and the current performance cannot meet the practical application requirements. Addressing these issues, this challenge focuses on ME and the macro-expression (MaE) spotting task. This year, in order to evaluate algorithms' performance more fairly, based on CAS(ME)2, SAMM Long Videos, SMIC-E-long, CAS(ME)3 and 4DME, we build an unseen cross-cultural long-video test set. All participating algorithms are required to run on this test set and submit their results on a leaderboard with a baseline result.

Micro-expressions are facial movements that are extremely short and not easily detected, which often reflect the genuine emotions of individuals. Micro-expressions are important cues for understanding real human emotions and can be used for non-contact, non-perceptual deception detection, or abnormal emotion recognition. It has broad application prospects in national security, judicial practice, health prevention, and clinical practice. However, micro-expression feature extraction and learning are highly challenging because they are typically short in duration, low intensity, and have local facial asymmetry. In addition, the intelligent micro-expression analysis combined with deep learning technology is also plagued by the problem of relatively small data samples. Not only is micro-expression elicitation very difficult, micro-expression annotation is also very time-consuming and laborious. More importantly, the micro-expression generation mechanism is not yet clear, which shackles the application of micro-expressions in real scenarios. FME'23 is the inaugural workshop in this area of research, with the aim of promoting interactions between researchers and scholars from within this niche area of research. This year we hope to discuss the growing ethical conversations when using face data, and how we can come to a consensus on micro-expression standards within affective computing.

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. Unfortunately, the small sample problem severely limits the automation of ME analysis. Furthermore, due to the brief and subtle nature of ME, ME spotting is a challenging task, and the performance is still not satisfactory yet. This challenge focuses on two tasks, i.e., the micro- and macro-expression spotting task, and the ME Generation task.

Facial expression spotting is the preliminary step for micro- and macro-expression analysis. The task of reliably spotting such expressions in video sequences is currently unsolved. Current best systems depend upon optical flow methods to extract regional motion features, before categorisation of that motion into a specific class of facial movement. Optical flow is susceptible to drift error, which introduces a serious problem for motions with long-term dependencies, such as high frame-rate macro-expression. We propose a purely deep learning solution which, rather than tracking frame differential motion, compares via a convolutional model, each frame with two temporally local reference frames. Reference frames are sampled according to calculated micro- and macro-expression duration. As baseline for MEGC2021 using leave-one-subject-out evaluation method, we show that our solution performed better in a high frame-rate (200 fps) SAMM long videos dataset (SAMM-LV) than a low frame-rate (30 fps) (CAS(ME)2) dataset. We introduce a new unseen dataset for MEGC2022 challenge (MEGC2022-testSet) and achieves F1-Score of 0.1531 as baseline result.

Facial micro-expression datasets lack consistency and standardisation, with different research groups using various experimental settings, in particular, where the datasets are varied in resolution and frame rates. To provide new insights into the roles of frame rate and resolution, we conduct an investigation into the use of different frame rates and resolution on current benchmark datasets (SMIC and CASME II). By using Temporal Interpolation Model, we subsample SMIC (original frame rate is 100 fps) to 50 fps and CASME II (original frame rate is 200 fps) into 100 fps and 50 fps. In addition, the resolution settings are adjusted to three scaling factors: 100% (original resolution), 75% and 50%. Three feature types are used to test the performance of these settings, which are Local Binary Patterns in Three Orthogonal Planes, 3D Histograms of Oriented Gradient and Histogram of Oriented Optical Flow. The results showed that the frame rate and resolution could affect the performance of micro-expression recognition, which behave distinctively dependent on feature types. This work provides new guidelines for future research in selecting frame rate, resolution and feature descriptors in micro-expressions recognition.

Micro-facial expressions are regarded as an important human behavioural event that can highlight emotional deception. Spotting these movements is difficult for humans and machines, however research into using computer vision to detect subtle facial expressions is growing in popularity. This paper proposes an individualised baseline micro-movement detection method using 3D Histogram of Oriented Gradients (3D HOG) temporal difference method. We define a face template consisting of 26 regions based on the Facial Action Coding System (FACS). We extract the temporal features of each region using 3D HOG. Then, we use Chi-square distance to find subtle facial motion in the local regions. Finally, an automatic peak detector is used to detect micro-movements above the proposed adaptive baseline threshold. The performance is validated on two FACS coded datasets: SAMM and CASME II. This objective method focuses on the movement of the 26 face regions. When comparing with the ground truth, the best result was an AUC of 0.7512 and 0.7261 on SAMM and CASME II, respectively. The results show that 3D HOG outperformed for micro-movement detection, compared to state-of-the-art feature representations: Local Binary Patterns in Three Orthogonal Planes and Histograms of Oriented Optical Flow.

This paper presents baseline results for the first Facial Micro-expressions Grand Challenge (MEGC) 2018 by evaluating LBP-TOP, HOOF and 3DHOG on CASME II and SAMM. We further improve the result of composite database evaluation (Task B of the challenge) by introducing selective block-based features fusion representation. Base on objective classes, this task combines CASME II and SAMM into a single composite database and uses Leave-One-Subject-Out crossvalidation to evaluate the performance. Our proposed method achieve F1-Score of 0.579, which outperformed LBP-TOP, HOOF and 3DHOG with 0.523, 0.527 and 0.436, respectively.

Micro-facial expressions are spontaneous, involuntary movements of the face when a person experiences an emotion but attempts to hide their facial expression, most likely in a high-stakes environment. Recently, research in this field has grown in popularity, however publicly available datasets of micro-expressions have limitations due to the difficulty of naturally inducing spontaneous micro-expressions. Other issues include lighting, low resolution and low participant diversity. We present a newly developed spontaneous micro-facial movement dataset with diverse participants and coded using the Facial Action Coding System. The experimental protocol addresses the limitations of previous datasets, including eliciting emotional responses from stimuli tailored to each participant. Dataset evaluation was completed by running preliminary experiments to classify micro-movements from non-movements. Results were obtained using a selection of spatio-temporal descriptors and machine learning. We further evaluate the dataset on emerging methods of feature difference analysis and propose an Adaptive Baseline Threshold that uses individualised neutral expression to improve the performance of micro-movement detection. In contrast to machine learning approaches, we outperform the state of the art with a recall of 0.91. The outcomes show the dataset can become a new standard for micro-movement data, with future work expanding on data representation and analysis.

Breast lesion detection using ultrasound imaging is considered an important step of computer-aided diagnosis systems. Over the past decade, researchers have demonstrated the possibilities to automate the initial lesion detection. However, the lack of a common dataset impedes research when comparing the performance of such algorithms. This paper proposes the use of deep learning approaches for breast ultrasound lesion detection and investigates three different methods: a Patch-based LeNet, a U-Net, and a transfer learning approach with a pretrained FCN-AlexNet. Their performance is compared against four state-of-the-art lesion detection algorithms (i.e., Radial Gradient Index, Multifractal Filtering, Rule-based Region Ranking, and Deformable Part Models). In addition, this paper compares and contrasts two conventional ultrasound image datasets acquired from two different ultrasound systems. Dataset A comprises 306 (60 malignant and 246 benign) images and Dataset B comprises 163 (53 malignant and 110 benign) images. To overcome the lack of public datasets in this domain, Dataset B will be made available for research purposes. The results demonstrate an overall improvement by the deep learning approaches when assessed on both datasets in terms of True Positive Fraction, False Positives per image, and F-measure.

Detecting micro-facial movements in a video sequence is the first step in realising a system that can pick out rapid movements automatically as a person is being recorded. This paper proposes a new method of micro-movement detection by applying Histogram of Oriented Gradients as a feature descriptor on our in-house high-speed video dataset of spontaneous micro facial movements. Firstly the algorithm aligns and crops faces for each video using automatic facial point detection and affine transformation. Then a de-noising algorithm is applied to each video before splitting them into blocks where the Histogram of Oriented Gradient features are calculated for each frame in every video block. The Chi-Squared distance measure is then used to calculate dissimilarity in the spatial appearance between frames at a set interval. The final feature vector is calculated after normalisation of the raw distance values and peak detection is applied to 'spot' micro-facial movements. An individualised baseline threshold is used to determine the value a peak must exceed to be classed as a movement. The result is compared with a benchmark algorithm - feature difference analysis techniques for micro-facial movements using Local Binary Patterns. Results indicate the proposed method achieves higher Recall of 0.8429 and F1-measure of 0.7672.