Facial Expression Recognition: One Attention-Modulated Contextual Spatial Information Network

With the work and study pressure on people increasing and the importance of mental health problems on the rise, facial expression analysis plays an important role in mental health auxiliary diagnosis and treatment (MHADT). This study proposes a facial expression localization and recognition model that integrates Main Directional Mean Optical Flow (MDMO) with the Transformer architecture. It addresses the problems of insufficient generalization ability and limited temporal modeling ability of traditional methods in facial expression recognition. The study is based on the authoritative Audio/Visual Emotion Challenge 2019 Depression Detection Sub-challenge (AVEC2019 DDS) dataset in the mental health field, which contains 163 training samples, 56 validation samples, and 56 test samples. With Explicit Shape Regression, Local Binary Patterns, Mnemonic Descent Method, Convolutional Neural Network, and benchmark models as comparison objects, it systematically evaluates the model's performance in error, accuracy, and processing speed. The results show that the model achieves the best performance in both facial expression localization and recognition tasks. The validation set errors are 7.26 and 6.85, the localization accuracy reaches 89.6%, and the recognition accuracy reaches 88.9%, which is significantly better than other methods. At the same time, its image processing time is 55 milliseconds (ms) and 44ms, balancing high precision and real-time performance. The study indicates that the fusion of MDMO and Transformer can effectively capture the spatial and temporal features of facial expressions. Thus, this fusion method provides an efficient, stable, and scalable technical solution for emotion recognition in MHADT. This improves both the FER and localization accuracy and effect. Besides, it provides a novel reference in both approaches and application level for the intelligent development of mental health evaluation.

We proposed a facial motion tracking and expression recognition system based on video data. By a 3D deformable facial model, the online statistical model (OSM) and cylinder head model (CHM) were combined to track 3D facial motion in the framework of particle filtering. For facial expression recognition, a fast and efficient algorithm and a robust and precise algorithm were developed. With the first, facial animation and facial expression were retrieved sequentially. After that facial animation was obtained, facial expression was recognized by static facial expression knowledge learned from anatomical analysis. With the second, facial animation and facial expression were simultaneously retrieved to increase the reliability and robustness with noisy input data. Facial expression was recognized by fusing static and dynamic facial expression knowledge, the latter of which was learned by training a multi-class expressional Markov process using a video database. The experiments showed that facial motion tracking by OSM+CHM is more pose robust than that by OSM, and the facial expression score of the robust and precise algorithm is higher than those of other state-of-the-art facial expression recognition methods.

In order to solve the problems of low average gradient and long recognition time in traditional facial expression recognition method, a multi-scale detail enhancement method for facial dynamic expression recognition of sprint athletes is proposed. A principal component analysis method was used to establish the facial expression feature subspace of sprinters, to project and reduce the dimension of the facial dynamic expression feature vector of sprinters, and to obtain the low frequency information and high frequency information of the facial image of sprinters by bilateral filtering. The multi-scale details of expression are enhanced by using side suppression network model and improving image S curve. The feature vector of facial dynamic expression is input into support vector machine to recognise the facial dynamic expression of sprinter. Experimental results show that the average value of annoying gradient is about 98 and the shortest time s. is about 1.9.

This paper presents a discriminative temporal topic model (DTTM) for facial expression recognition. Our DTTM is developed by introducing temporal and categorical information into Latent Dirichlet Allocation (LDA) topic model. Temporal information is integrated by placing an asymmetric Dirichlet prior over document-topic distributions. The discriminative ability is improved by a supervised term weighting scheme. We describe the resulting DTTM in detail and show how it can be applied to facial expression recognition. Experiments on CMU expression database illustrate that the proposed DTTM is very effective in facial expression recognition.KeywordsFacial ExpressionTopic ModelLatent Dirichlet AllocationFacial Expression RecognitionActive Appearance ModelThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

BackgroundAlcohol abuse and dependence can cause a wide variety of cognitive, psychomotor, and visual-spatial deficits. It is questionable whether this condition is associated with impairments in the recognition of affective and/or emotional information. Such impairments may promote deficits in social cognition and, consequently, in the adaptation and interaction of alcohol abusers with their social environment. The aim of this systematic review was to systematize the literature on alcoholics’ recognition of basic facial expressions in terms of the following outcome variables: accuracy, emotional intensity, and latency time.MethodsA systematic literature search in the PsycINFO, PubMed, and SciELO electronic databases, with no restrictions regarding publication year, was employed as the study methodology.ResultsThe findings of some studies indicate that alcoholics have greater impairment in facial expression recognition tasks, while others could not differentiate the clinical group from controls. However, there was a trend toward greater deficits in alcoholics. Alcoholics displayed less accuracy in recognition of sadness and disgust and required greater emotional intensity to judge facial expressions corresponding to fear and anger.ConclusionThe current study was only able to identify trends in the chosen outcome variables. Future studies that aim to provide more precise evidence for the potential influence of alcohol on social cognition are needed.

Background: Individuals with child maltreatment (CM) experiences show alterations in emotion recognition (ER). However, previous research has mainly focused on populations with specific mental disorders, which makes it unclear whether alterations in the recognition of facial expressions are related to CM, to the presence of mental disorders or to the combination of CM and mental disorders, and on ER of emotional, rather than neutral facial expressions. Moreover, commonly, recognition of static stimulus material was researched. Objective: We assessed recognition of dynamic (closer to real life) negative, positive and neutral facial expressions in individuals characterised by CM, rather than a specific mental disorder. Moreover, we assessed whether they show a negativity bias for neutral facial expressions and whether the presence of one or more mental disorders affects recognition. Methods: Ninety-eight adults with CM experiences (CM+) and 60 non-maltreated (CM−) adult controls watched 200 non-manipulated coloured video sequences, showing 20 neutral and 180 emotional facial expressions, and indicated whether they interpreted each expression as neutral or as one of eight emotions. Results: The CM+ showed significantly lower scores in the recognition of positive, negative and neutral facial expressions than the CM− group (p < .050). Furthermore, the CM+ group showed a negativity bias for neutral facial expressions (p < .001). When accounting for mental disorders, significant effects stayed consistent, except for the recognition of positive facial expressions: individuals from the CM+ group with but not without mental disorder scored lower than controls without mental disorder. Conclusions: CM might have long-lasting influences on the ER abilities of those affected. Future research should explore possible effects of ER alterations on everyday life, including implications of the negativity bias for neutral facial expressions on emotional wellbeing and relationship satisfaction, providing a basis for interventions that improve social functioning.

In order to achieve high recognition rate, most facial expression recognition (FER) methods generate sufficient labeled facial images based on generative adversarial networks (GAN) to train model. However, these methods do not estimate the facial pose before passing the images to the generator, which affects the quality of generated images. And mode collapse is prone to occur during the training process, leading to generate a single-style facial images. To solve these problems, a FER model is proposed based on pose conditioned dendritic convolution neural network (PCD-CNN) with pose and expression. Before passing the facial images to the generator, PCD-CNN was used to process facial images, effectively estimating the facial landmarks to detect face and disentangle the pose. In order to accelerate the training speed of the model, PCD-CNN was based on the ShuffleNet-v2 framework. Every landmark of facial image was modeled by a separate ShuffleNet-DeconvNet, maintaining better performance with fewer parameters. To solve the mode collapse during image generation, we theoretically analyzed the causes, and implemented mini-batch processing on the discriminator in the model and directly calculated the statistical characteristics of the mini-batch samples. Experiments were carried out on the Multi-PIE and BU-3DFE facial expression datasets. Compared with current advanced methods, our method achieves higher accuracy 93.08%, and the training process is more stable.

Facial expression recognition (FER) is one of the most popular research interests in Computer Science and can be appliedin many real time applications. Most of the FER systemsapply machine learning techniques such asConvolutional Neural Network and Support Vector Machine. Although there are many researches on recognition of facialexpression, it is still a challenging task for computerprograms and also mobile applications. Therefore, this paper proposed an Android application for FER using deep learning technique that can recognize three facial expressions whichare happy, angry and surprise. FER model has been trained byusing Personal Image Classifier (PIC) based on facial expression dataset from Kaggle and achieved 95% accuracy. The trained model was later applied for Android application development in MIT App Inventor. The proposed Android application for FER has been successfully built, tested, and uploaded into Google Play Store

Facial expressions recognition and synthesis are important research fields to study how human beings reflect to environments in affective computing. With the rapid development of mathematical theory on multivariate statistics and multi-media technology especially image processing, facial expressions recognition researchers have achieved many useful results. Recently studies show that approaches to facial modeling and expressions recognition and synthesis analysis could be adapted to control security or even real-time health monitoring in the real world. Similarity, how to achieve facial expressions recognition and synthesis for independent-free mechanism is a central design in general. Based on the cognitive analysis of independent user's affective facial recognition researches we proposed an emotions composition model to dope out what are the user's new affective facial expressions. At this point, principal component, cluster and discriminate analysis were applied to show and verify independent user's affective expressions can be composited for synthesis by basic facial expressions such as happy, neutral and unhappy. Experiments were conducted to prove our models to be very significant.

Recent deep neural networks based methods have achieved state-of-the-art performance on various facial expression recognition tasks. Despite such progress, previous researches for facial expression recognition have mainly focused on analyzing color recording videos only. However, the complex emotions expressed by people with different skin colors under different lighting conditions through dynamic facial expressions can be fully understandable by integrating information from multi-modal videos. We present a novel method to estimate dimensional emotion states, where color, depth, and thermal recording videos are used as a multi-modal input. Our networks, called multi-modal recurrent attention networks (MRAN), learn spatiotemporal attention volumes to robustly recognize the facial expression based on attention-boosted feature volumes. We leverage the depth and thermal sequences as guidance priors for color sequence to selectively focus on emotional discriminative regions. We also introduce a novel benchmark for multi-modal facial expression recognition, termed as multi-modal arousal-valence facial expression recognition (MAVFER), which consists of color, depth, and thermal recording videos with corresponding continuous arousal-valence scores. The experimental results show that our method can achieve the state-of-the-art results in dimensional facial expression recognition on color recording datasets including RECOLA, SEWA and AFEW, and a multi-modal recording dataset including MAVFER.

Facial expression recognition (FER) is utilized in various fields that analyze facial expressions. FER is attracting increasing attention for its role in improving the convenience in human life. It is widely applied in human–computer interaction tasks. However, recently, FER tasks have encountered certain data and training issues. To address these issues in FER, few-shot learning (FSL) has been researched as a new approach. In this paper, we focus on analyzing FER techniques based on FSL and consider the computational complexity and processing time in these models. FSL has been researched as it can solve the problems of training with few datasets and generalizing in a wild-environmental condition. Based on our analysis, we describe certain existing challenges in the use of FSL in FER systems and suggest research directions to resolve these issues. FER using FSL can be time efficient and reduce the complexity in many other real-time processing tasks and is an important area for further research.

In order to promote the utilization of lifelog videos, an effective retrieval framework of the emotional scenes, which are considered to be important scenes, is proposed in this paper. The proposed method is based on facial expression recognition since the emotional scenes can be detected by taking the facial expressions into consideration. Most of conventional facial expression recognition methods require a large amount of training data to construct a recognition model. Adopting such methods for large-scale video databases is unrealistic because preparing sufficient training data requires considerable human efforts. We introduce an unsupervised machine learning framework to solve this issue by making it possible to construct a facial expression recognition model without any training data set. The proposed method is evaluated through an emotional scene detection experiment. A prototype of the emotional scene retrieval system based on the proposed emotional scene detection method is implemented.

Since the recognition of facial expressions from images and videos has great commercial and academic potential, it has become a common topic of discussion in the fields of artificial intelligence and computer vision. Considering mobile phones, versatile models should be small and quick, and precise as well. Until recently, some works have proposed various facial expression recognition models, but although these works perform better, it can still be said that there are incredible opportunities for improvement. This paper proposes a facial expression recognition method based on Automated mobile neural architecture search (MNAS). On the FER2013 dataset, our MnasNet model achieved an accuracy of 70.82%.

Facial expression recognition is a practical application in the field of artificial intelligence. This article focuses on the introduction of the principles of each facial expression recognition method based on deep learning in chronological order, as well as the advantages and disadvantages of each method. The methods of facial expression recognition based on deep learning introduced in this article are as follows: Li Dunyu [1] proposed a method of LBP and BP neural network to realize facial expression recognition. And then the convolutional neural network is widely used, Zhou Benjun [2] merged LBP and convolutional neural network to realize facial expression recognition; Liu [3] establish a model that combines a recurrent neural network with a 3D convolutional neural network, and added a timeline so that the machine can extract and recognize facial expressions in a video; Jung [4] proposed to combine the convolutional neural network and the deep neural network to form a new neural network DTAGN, by using this new network he completes facial expression recognition; Shi Xugan [5] proposed a model of combining deep belief network with multi-layer perceptron (MLP), and successfully applied it to the research of facial expression recognition. By utilizing joint training of feature points and multiple networks Xia Tian [6] finish the research; Zhang Feifei [7] and other scholars proposed multi-pose facial expression recognition based on generating an adversarial network.

Emotions are pivotal in the learning process, highlighting the importance of identifying students' emotional states within educational settings. While neural network models, particularly those rooted in deep learning, have demonstrated remarkable accuracy in detecting primary emotions like happiness, sadness, fear, disgust, and anger from facial expressions in videos, these emotions occur infrequently in learning environments. Conversely, cognitive emotions such as engagement, confusion, frustration, and boredom are significantly more prevalent, transpiring five times more frequently than basic emotions. However, unlike basic emotions which are relatively distinct, cognitive emotions present a subtler distinction, necessitating the utilization of more sophisticated models for accurate recognition. The proposed work presents an efficient Facial Expression Recognition (FER) model for monitoring the student engagement in a learning environment by considering their facial expressions like boredom, frustration, confusion and engagement. The proposed methodology includes certain pre-processing steps followed by facial expression recognition founded on Efficient-Net B3 CNN in which the learning parameters are optimized using Circle-Inspired Optimization Algorithm (CIOA). Finally, the post processing stage estimates the frame-wise group engagement level (GEL) of students based on certain expression labels. Based on the acquired results, it is noted that the suggested Efficient-Net B3 CNN-CIOA based FER model provides promising results in terms of accuracy by 99.5%, precision by 99.2%, recall by 99.5% and f1-score by 99.6%, when compared with some state-of-art facial expression recognition approaches. Also, the suggested approach computational complexity is very much less than the compared existing approaches.