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1. Micro-Expression Classification based on Landmark Relations with Graph Attention Convolutional Network

   Kumar, Ankith Jain ; Bhanu, Bir ( June 2021 , Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops)

   null (Ed.)

   Facial micro-expressions are brief, rapid, spontaneous gestures of the facial muscles that express an individual's genuine emotions. Because of their short duration and subtlety, detecting and classifying these micro-expressions by humans and machines is difficult. In this paper, a novel approach is proposed that exploits relationships between landmark points and the optical flow patch for the given landmark points. It consists of a two-stream graph attention convolutional network that extracts the relationships between the landmark points and local texture using an optical flow patch. A graph structure is built to draw out temporal information using the triplet of frames. One stream is for node feature location, and the other one is for a patch of optical-flow information. These two streams (node location stream and optical flow stream) are fused for classification. The results are shown on, CASME II and SAMM, publicly available datasets, for three classes and five classes of micro-expressions. The proposed approach outperforms the state-of-the-art methods for 3 and 5 categories of expressions. 

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2. Relational edge-node graph attention network for classification of micro-expressions

   Kumar, A.J.R. ; Bhanu, B./Bhanu ( June 2023 , Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops.)

   Facial micro-expressions (MEs) refer to subtle, transient, and involuntary muscle movements expressing a per-son’s true feelings. This paper presents a novel two-stream relational edge-node graph attention network-based approach to classify MEs in a video by selecting the high-intensity frames and edge-node features that can provide valuable information about the relationship between nodes and structural information in a graph structure. The pa-per examines the impact of different edge-node features and their relationships on the graphs. The first step involves extracting high-intensity-emotion frames from the video using optical flow. Second, node feature embeddings are calculated using the node location coordinate features and the patch size information of the optical flow across each node location. Additionally, we obtain the global and local structural similarity score using the jaccard’s similarity score and radial basis function as the edge features. Third, a self-attention graph pooling layer helps to remove the nodes with lower attention scores based on the top-k selection. As the final step, the network employs a two-stream edge-node graph attention network that focuses on finding correlations among the edge and node features, such as landmark coordinates, optical flow, and global and local edge features. A three-frame graph structure is designed to obtain spatio-temporal information. For 3 and 5 expression classes, the results are compared for SMIC and CASME II databases. 

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3. Challenging the Classical View: Recognition of Identity and Expression as Integrated Processes

   https://doi.org/10.3390/brainsci13020296  

   Schwartz, Emily ; O’Nell, Kathryn ; Saxe, Rebecca ; Anzellotti, Stefano ( February 2023 , Brain Sciences)

   Recent neuroimaging evidence challenges the classical view that face identity and facial expression are processed by segregated neural pathways, showing that information about identity and expression are encoded within common brain regions. This article tests the hypothesis that integrated representations of identity and expression arise spontaneously within deep neural networks. A subset of the CelebA dataset is used to train a deep convolutional neural network (DCNN) to label face identity (chance = 0.06%, accuracy = 26.5%), and the FER2013 dataset is used to train a DCNN to label facial expression (chance = 14.2%, accuracy = 63.5%). The identity-trained and expression-trained networks each successfully transfer to labeling both face identity and facial expression on the Karolinska Directed Emotional Faces dataset. This study demonstrates that DCNNs trained to recognize face identity and DCNNs trained to recognize facial expression spontaneously develop representations of facial expression and face identity, respectively. Furthermore, a congruence coefficient analysis reveals that features distinguishing between identities and features distinguishing between expressions become increasingly orthogonal from layer to layer, suggesting that deep neural networks disentangle representational subspaces corresponding to different sources. 

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4. Depth Videos for the Classification of Micro-Expressions

   https://doi.org/10.1109/ICPR48806.2021.9412976  

   Rakesh Kumar, Ankith Jain ; Bhanu, Bir ; Casey, Christopher ; Cheung, Sierra Grace ; Seitz, Aaron ( January 2021 , 2020 25th International Conference on Pattern Recognition (ICPR))

   null (Ed.)

   Facial micro-expressions are spontaneous, subtle, involuntary muscle movements occurring briefly on the face. The spotting and recognition of these expressions are difficult due to the subtle behavior, and the time duration of these expressions is about half a second, which makes it difficult for humans to identify them. These micro-expressions have many applications in our daily life, such as in the field of online learning, game playing, lie detection, and therapy sessions. Traditionally, researchers use RGB images/videos to spot and classify these micro-expressions, which pose challenging problems, such as illumination, privacy concerns and pose variation. The use of depth videos solves these issues to some extent, as the depth videos are not susceptible to the variation in illumination. This paper describes the collection of a first RGB-D dataset for the classification of facial micro-expressions into 6 universal expressions: Anger, Happy, Sad, Fear, Disgust, and Surprise. This paper shows the comparison between the RGB and Depth videos for the classification of facial micro-expressions. Further, a comparison of results shows that depth videos alone can be used to classify facial micro-expressions correctly in a decision tree structure by using the traditional and deep learning approaches with good classification accuracy. The dataset will be released to the public in the near future. 

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5. Edge Convolutional Network for Facial Action Intensity Estimation

   https://doi.org/10.1109/FG.2018.00034  

   Li, Liandong ; Baltrusaitis, Tadas ; Sun, Bo ; Morency, Louis-Philippe ( January 2018 , Proceedings of the IEEE Conference on Automatic Face and Gesture Recognition (FG))

   In this paper, we propose a novel convolutional neural architecture for facial action unit intensity estimation. While Convolutional Neural Networks (CNNs) have shown great promise in a wide range of computer vision tasks, these achievements have not translated as well to facial expression analysis, with hand crafted features (e.g. the Histogram of Orientated Gradient) still being very competitive. We introduce a novel Edge Convolutional Network (ECN) that is able to capture subtle changes in facial appearance. Our model is able to learn edge-like detectors that can capture subtle wrinkles and facial muscle contours at multiple orientations and frequencies. The core novelty of our ECN model is in its first layer which integrates three main components: an edge filter generator, a receptive gate and a filter rotator. All the components are differentiable and our ECN model is end-to-end trainable and learns the important edge detectors for facial expression analysis. Experiments on two facial action unit datasets show that the proposed ECN outperforms state-of-the-art methods for both AU intensity estimation tasks. 

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