Facial Expression Recognition Method Research Articles

Eye Strain Expression Classification using Attention Capsule Network for Adapting Screen Vision

Beside the conventional facial expression recognition methods, the research focuses on developing a system for recognizing various eye expressions under different screen conditions. This research deals with the use of Capsule Network (a recent Deep Learning algorithm) to enhance facial expression recognition capabilities and to develop adaptive screen technologies aimed at mitigating digital eye strain. The main objective of this research is to engineer a sophisticated system that employs the capabilities of Capsule Nets to recognize the various expressions that user makes and based on the recognized expression, dynamically modify screen settings, ensuring optimal user visual comfort. The research primarily concentrates on the exploration and application of various Capsule Net architectures designed for the recognition of expressions related to eye strain. The baseline model utilized elementary convolutional layers which feed into subsequent fully connected layers for the task of classification. The model has since been refined by incorporating advanced techniques such as attention mechanisms and more sophisticated network architectures where the classification is done by Capsule Network. Results have demonstrated a modest enhancement in the Capsule Net’s predictive performance, attributed to its superior spatial and hierarchical processing of facial features, in comparison to conventional deep learning approaches. The final model has an accuracy of 82.27%. As a final system the model has been deployed to an application to process frames from video camera in the device and make prediction to prompt the notifications or recommendations.

Lightweight facial expression estimation for mobile computing in portable device

AbstractFacial expression recognition has been studied for many years, especially with the development of deep learning. However, the existing researches still have the following two issues. Firstly, the intensity of facial expression is neglected. Secondly, the deep learning based approaches cannot be directly deployed in the devices with limited resources. In order to tackle these two issues, this paper proposes a lightweight facial expression estimation method using a shallow ordinal regression algorithm, which is deployed in a portable smart device for mobile computing in IoTs. Compared with classification based facial expression recognition methods, ordinal regression considers the intensity of facial expression to achieve better mean absolute error (MAE), which is validated by experiments on several public facial expression datasets. The simulation in portable device also demonstrates its effectiveness for mobile computing.

CSINet: Channel–Spatial Fusion Networks for Asymmetric Facial Expression Recognition

Occlusion or posture change of the face in natural scenes has typical asymmetry; however, an asymmetric face plays a key part in the lack of information available for facial expression recognition. To solve the problem of low accuracy of asymmetric facial expression recognition, this paper proposes a fusion of channel global features and a spatial local information expression recognition network called the “Channel–Spatial Integration Network” (CSINet). First, to extract the underlying detail information and deepen the network, the attention residual module with a redundant information filtering function is designed, and the backbone feature-extraction network is constituted by module stacking. Second, considering the loss of information in the local key area of face occlusion, the channel–spatial fusion structure is constructed, and the channel features and spatial features are combined to enhance the accuracy of occluded facial recognition. Finally, before the full connection layer, more local spatial information is embedded into the global channel information to capture the relationship between different channel–spatial targets, which improves the accuracy of feature expression. Experimental results on the natural scene facial expression data sets RAF-DB and FERPlus show that the recognition accuracies of the modeling approach proposed in this paper are 89.67% and 90.83%, which are 13.24% and 11.52% higher than that of the baseline network ResNet50, respectively. Compared with the latest facial expression recognition methods such as CVT, PACVT, etc., the method in this paper obtains better evaluation results of masked facial expression recognition, which provides certain theoretical and technical references for daily facial emotion analysis and human–computer interaction applications.

Multi-Energy Guided Image Translation with Stochastic Differential Equations for Near-Infrared Facial Expression Recognition

Illumination variation has been a long-term challenge in real-world facial expression recognition (FER). Under uncontrolled or non-visible light conditions, near-infrared (NIR) can provide a simple and alternative solution to obtain high-quality images and supplement the geometric and texture details that are missing in the visible (VIS) domain. Due to the lack of large-scale NIR facial expression datasets, directly extending VIS FER methods to the NIR spectrum may be ineffective. Additionally, previous heterogeneous image synthesis methods are restricted by low controllability without prior task knowledge. To tackle these issues, we present the first approach, called for NIR-FER Stochastic Differential Equations (NFER-SDE), that transforms face expression appearance between heterogeneous modalities to the overfitting problem on small-scale NIR data. NFER-SDE can take the whole VIS source image as input and, together with domain-specific knowledge, guide the preservation of modality-invariant information in the high-frequency content of the image. Extensive experiments and ablation studies show that NFER-SDE significantly improves the performance of NIR FER and achieves state-of-the-art results on the only two available NIR FER datasets, Oulu-CASIA and Large-HFE.

A novel deep learning model to improve the recognition of students’ facial expressions in online learning environments

With the fast development of artificial intelligence and emerging technologies, automatic recognition of students’ facial expressions has received increased attention. Facial expressions are a kind of external manifestation of emotional states. It is important for teachers to assess students’ emotional states and adjust teaching activities accordingly. However, existing methods for automatic facial expression recognition have the limitations of low accuracy of recognition and poor feature extraction. To address the problem, this study proposed a novel deep learning model called DenseNetX-CBAM to improve facial expression recognition by utilizing a variant of densely connected convolutional networks (DenseNet) to reduce unnecessary parameters and strengthen the reuse of expression features between networks; moreover, convolutional block attention module (CBAM) was integrated to allow the networks to focus on important special regions and important channels when representing features. The proposed model was tested using 217 video clips of 33 students in an online course. The results demonstrated promising effects of the method in improving the accuracy of facial expression recognition, which can help teachers to accurately recognize students’ emotions and provide real-time adjustment in online learning environments.

Improving recognition accuracy for facial expressions using scattering wavelet

One of the most evident and meaningful feedback about people’s emotions is through facial expressions. Facial expression recognition is helpful in social networks, marketing, and intelligent education systems. The use of Deep Learning based methods in facial expression identification is widespread, but challenges such as computational complexity and low recognition rate plague these methods. Scatter Wavelet is a type of Deep Learning that extracts features from Gabor filters in a structure similar to convolutional neural networks. This paper presents a new facial expression recognition method based on wavelet scattering that identifies six states: anger, disgust, fear, happiness, sadness, and surprise. The proposed method is simulated using the JAFFE and CK+ databases. The recognition rate of the proposed method is 99.7%, which indicates the superiority of the proposed method in recognizing facial expressions.

Effective Facial Expression Recognition System Using Machine Learning

The co Facial expression recognition (FER) is a topic that has seen a lot of study in computer vision and machine learning. In recent years, deep learning techniques have shown remarkable progress on FER tasks. With this abstract, A Novel Is Advised By Us FER method that combines combined use of k-nearest neighbours and long short-term memory algorithms better efficiency and accurate facial expression recognition. The proposed system features two primary steps—feature extraction and classification—to get results. When extracting features, we extract features from the facial images using the Local Binary Patterns (LBP) algorithm. LBP is a simple yet powerful feature extraction technique that captures texture information from the image. In the classification stage, we use the KNN and LSTM algorithms for facial expression recognition. KNN is a simple and effective classification algorithm that finds the k closest to the given value neighbours to the test training-set-sample and assigning it to the class that is most frequent among its neighbours. However, KNN has limitations in handling temporal information. To address this limitation, we propose to use LSTM, which is a subclass of RNNs capable of capturing temporal relationships in time series data. The LSTM network takes as input the LBP features of a sequence of facial images and processes them through a series of LSTM cells to estimate the ultimate coding of the phrase. We examine the planned and system on two publicly available records: the CK+ and the Oulu-CASIA datasets. According on the experimental findings, the proposed system achieves performance at the cutting edge on both datasets. The proposed system performs better than other state-of-the-art methods, including those that use deep learning systems, quantitatively, in terms of F1-score and precision.In conclusion, the proposed FER system that combines KNN and LSTM algorithms achieves high accuracy and an F1 score in recognising facial expressions from sequences of images. This system can be used in many contexts, including human-computer interaction, emotion detection, and behaviour analysis.

Advances in Facial Expression Recognition: A Survey of Methods, Benchmarks, Models, and Datasets

Recent technological developments have enabled computers to identify and categorize facial expressions to determine a person’s emotional state in an image or a video. This process, called “Facial Expression Recognition (FER)”, has become one of the most popular research areas in computer vision. In recent times, deep FER systems have primarily concentrated on addressing two significant challenges: the problem of overfitting due to limited training data availability, and the presence of expression-unrelated variations, including illumination, head pose, image resolution, and identity bias. In this paper, a comprehensive survey is provided on deep FER, encompassing algorithms and datasets that offer insights into these intrinsic problems. Initially, this paper presents a detailed timeline showcasing the evolution of methods and datasets in deep facial expression recognition (FER). This timeline illustrates the progression and development of the techniques and data resources used in FER. Then, a comprehensive review of FER methods is introduced, including the basic principles of FER (components such as preprocessing, feature extraction and classification, and methods, etc.) from the pro-deep learning era (traditional methods using handcrafted features, i.e., SVM and HOG, etc.) to the deep learning era. Moreover, a brief introduction is provided related to the benchmark datasets (there are two categories: controlled environments (lab) and uncontrolled environments (in the wild)) used to evaluate different FER methods and a comparison of different FER models. Existing deep neural networks and related training strategies designed for FER, based on static images and dynamic image sequences, are discussed. The remaining challenges and corresponding opportunities in FER and the future directions for designing robust deep FER systems are also pinpointed.

An efficient semi-dynamic ensemble pruning method for facial expression recognition

An efficient semi-dynamic ensemble pruning method for facial expression recognition

Correction to: Deep Learning Method of Facial Expression Recognition Based on Gabor Filter Bank Combined with PCNN

Correction to: Deep Learning Method of Facial Expression Recognition Based on Gabor Filter Bank Combined with PCNN

Learning informative and discriminative semantic features for robust facial expression recognition

Facial expression recognition (FER) becomes challenging in real-world scenarios, which requires learning informative and discriminative features from challenging datasets to obtain robust facial expression recognition. In this paper, we propose an Informative and Discriminative Semantic Features Learning (IDSFL) network for FER against occlusion and head pose in the wild. Specifically, IDSFL aims to mine informative and discriminative semantic features from both low and high levels learned features to learn robust representations. First, a multi-channel feature (MCF) modulator incorporating low-level Gabor features is introduced to learn informative semantic features by capturing adequate diverse and detailed information. Additionally, a specific emotion-aware (SEA) module is proposed to learn discriminative semantic features by aggregating high-level emotion-specific features to focus on each expression category. Thus, IDSFL can collaboratively learn informative and discriminative representations. Extensive experiments on challenging in-the-wild datasets, including RAF-DB, FERPlus and AffectNet-7, demonstrate that our proposed method outperforms most state-of-the-art FER methods.

A method for facial expression recognition of image sequence based on spatial features

Facial expression recognition is the foundation of human emotion recognition, which has become a hot topic in the field of artificial intelligence in recent years. To some extent, facial expressions can be regarded as the process of facial muscle changes. Image sequence contains richer expression contents compared with a single image. So the expression recognition based on image sequence can yield more accurate results. A new method for facial expression recognition is proposed in this paper, which is based on spatial feature for image sequence. The method consists of four steps. Firstly, Siamese neural network is used to construct an evaluation model for changes of expression intensity, which extracts an appropriate image sequence from the video. Secondly, a convolutional neural network with attention mechanism is designed and trained, which is used to extract spatial features from each image in the sequence. Then, the spatial features of multiple images are fused. Finally, the fusion results are put into a convolutional neural network to recognize the facial expressions. This method is validated on CK+ dataset and the experimental results show that it’s more accurate than several other methods.

Auto Diagnosis of Parkinson's Disease Via a Deep Learning Model Based on Mixed Emotional Facial Expressions.

Parkinson's disease (PD) is a common degenerative disease of the nervous system in the elderly. The early diagnosis of PD is very important for potential patients to receive prompt treatment and avoid the aggravation of the disease. Recent studies have found that PD patients always suffer from emotional expression disorder, thus forming the characteristics of "masked faces". Based on this, we thus propose an auto PD diagnosis method based on mixed emotional facial expressions in the paper. Specifically, the proposed method is cast into four steps: Firstly, we synthesize virtual face images containing six basic expressions (i.e., anger, disgust, fear, happiness, sadness, and surprise) via generative adversarial learning, in order to approximate the premorbid expressions of PD patients; Secondly, we design an effective screening scheme to assess the quality of the above synthesized facial expression images and then shortlist the high-quality ones; Thirdly, we train a deep feature extractor accompanied with a facial expression classifier based on the mixture of the original facial expression images of the PD patients, the high-quality synthesized facial expression images of PD patients, and the normal facial expression images from other public face datasets; Finally, with the well-trained deep feature extractor, we thus adopt it to extract the latent expression features for six facial expression images of a potential PD patient to conduct PD/non-PD prediction. To show real-world impacts, we also collected a new facial expression dataset of PD patients in collaboration with a hospital. Extensive experiments are conducted to validate the effectiveness of the proposed method for PD diagnosis and facial expression recognition.

Relationship-Guided Knowledge Transfer for Class-Incremental Facial Expression Recognition.

Human emotions contain both basic and compound facial expressions. In many practical scenarios, it is difficult to access all the compound expression categories at one time. In this paper, we investigate comprehensive facial expression recognition (FER) in the class-incremental learning paradigm, where we define well-studied and easily-accessible basic expressions as initial classes and learn new compound expressions incrementally. To alleviate the stability-plasticity dilemma in our incremental task, we propose a novel Relationship-Guided Knowledge Transfer (RGKT) method for class-incremental FER. Specifically, we develop a multi-region feature learning (MFL) module to extract fine-grained features for capturing subtle differences in expressions. Based on the MFL module, we further design a basic expression-oriented knowledge transfer (BET) module and a compound expression-oriented knowledge transfer (CET) module, by effectively exploiting the relationship across expressions. The BET module initializes the new compound expression classifiers based on expression relevance between basic and compound expressions, improving the plasticity of our model to learn new classes. The CET module transfers expression-generic knowledge learned from new compound expressions to enrich the feature set of old expressions, facilitating the stability of our model against forgetting old classes. Extensive experiments on three facial expression databases show that our method achieves superior performance in comparison with several state-of-the-art methods.

Unconstrained Facial Expression Recognition With No-Reference De-Elements Learning

Most unconstrained facial expression recognition (FER) methods take original facial images as inputs to learn discriminative features by well-designed loss functions, which cannot reflect important visual information in faces. Although existing methods have explored the visual information of constrained facial expressions, there is no explicit modeling of what visual information is important for unconstrained FER. To find out valuable information of unconstrained facial expressions, we pose a new problem of no-reference de-elements learning: we decompose any unconstrained facial image into the facial expression element and a neutral face without the reference of corresponding neutral faces. Importantly, the element provides visualization results to understand important facial expression information and improves the discriminative power of features. Moreover, we propose a simple yet effective <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</b> e- <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</b> lements <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Net</b> work (DENet) to learn the element and introduce appropriate constraints to overcome no ground truth of corresponding neutral faces during the de-elements learning. We extensively evaluate the proposed method on in-the-wild FER datasets including RAF-DB, AffectNet, SFEW and FERPlus. The comparable results show that our method is promising to improve classification performance and achieves equivalent performance compared with state-of-the-art methods. Also, we demonstrate the strong generalization performance on realistic occlusion and pose variation datasets and the cross-dataset evaluation.

Adaptive semi-supervised facial expression recognition method based on improved ResNet50

Adaptive semi-supervised facial expression recognition method based on improved ResNet50

Accurate facial expression recognition method based on perceptual hash algorithm

Accurate facial expression recognition method based on perceptual hash algorithm

Facial expression recognition method based on multi-level feature fusion of high-resolution images

Facial expression recognition method based on multi-level feature fusion of high-resolution images

Accurate facial expression recognition method based on perceptual hash algorithm

Accurate facial expression recognition method based on perceptual hash algorithm

Pose-Aware Facial Expression Recognition Assisted by Expression Descriptions

Although expression descriptions provide additional information about facial behaviors despite of different poses, and pose features are beneficial to adapt to pose variety, neither has been fully leveraged in facial expression recognition. This paper proposes a pose-aware text-assisted facial expression recognition method using cross-modality attention. Specifically, the method contains three components. The pose feature extractor extracts pose-related features from facial images, and then cooperates with a fully-connected layer for pose classification. When poses can be clearly discriminated and classified, features obtained from the extractor can represent the corresponding poses. To eliminate bias due to appearance and illumination, cluster centers are taken as the final pose features. The text feature extractor obtains embeddings from expression descriptions. These descriptions are first passed through Intra-Exp attention to obtain preliminary embeddings. To leverage the correlations among expressions, all expression embeddings are then concatenated and passed through Inter-Exp attention. The cross-modality module attempts to learn attention maps that distinguish the importance of facial regions by using prior knowledge about poses and expression descriptions. The image features weighted by the attention maps are utilized to recognize pose and expression jointly. Experiments on three benchmark datasets demonstrate the superiority of the proposed method.