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Urban green-blue spaces (UGBS) are increasingly recognized for their benefits to physical and mental well-being. However, research on real-time gender-specific emotional responses to UGBS remains limited. To address this gap, a dual-method approach combining facial expression recognition (FER) and self-reported measures to investigate gender differences in real-time emotional evaluations of UGBS was developed. Using static images from Google Street View as stimuli, a self-reporting experiment involving 108 participants provided insights into subjective emotional experiences. Subsequently, a FER experiment, utilizing 360-degree video stimuli, captured over two million data points, validating the feasibility and advantages of real-time emotion monitoring. The findings revealed distinct gender-specific emotional patterns: women experienced stronger pleasant emotions and preferred scenes evoking higher arousal, while men demonstrated sharper responses and rated scenes with peak valence emotions more favorably. Grass elicited relaxation and delight in women and arousal in men, whereas blue spaces induced calmness across genders, with men reporting greater relaxation as water content increased. The study underscores the potential of FER technology in assessing real-time emotional responses, providing actionable insights for inclusive urban planning. By integrating advanced tools and participatory design approaches, urban planners can develop strategies that enhance emotional well-being and create livable cities that support diverse user needs. [ABSTRACT FROM AUTHOR]

Facial expression recognition finds extensive applications in human-computer interaction, education, and autonomous driving. Nevertheless, occlusion and pose changes during expression recognition can lead to the loss of information about different facial parts, obfuscation, and the disruption of local relationships, frequently resulting in a decline in recognition accuracy. Existing research methods encounter difficulties in directing attention to affected areas and capturing decisive features and their global relationships. To tackle these issues, this paper presents an Expression Recognition model known as ADMF-ER (Expression Recognition model fusing Adaptive Dropout and Multi-level Features), which integrates hierarchical and global features. Through hierarchical and global feature modeling, the model enhances its ability to recognize facial expressions under complex conditions such as occlusion and pose changes. It constructs a Multi-scale Residual Attention (MSRA) module to extract features at different levels and scales, with the aim of learning rich semantic and geometric features. Additionally, the model incorporates spatial and channel attention mechanisms, enabling the network to selectively focus on and emphasize affected local areas and capture key features. Finally, global features and relationships are learned via the vision Transformer structure and dynamically pruned using Dropout. As training advances, the pruning intensity increases, facilitating a better understanding of overall facial expressions. Experimental results indicate that the method proposed in this paper achieves remarkably superior performance on RAF-DB, FERPlus, and AffectNet datasets. At the same time, the method demonstrates excellent robustness in the evaluation of CK+ across datasets. [ABSTRACT FROM AUTHOR]

In learning environment, instructor's emotions may impact the relationship with students. A wide range of research has been conducted on the facial expression of the student, but the influence of teacher's facial expression remains an unexamined area. Facial expression detection has the ability to detect the impact of an instructor's emotions in a learning environment. During lecture delivery, manual evaluation of the instructor behavior not only improves the learning environment, but can also save time and resources used in manual techniques. Therefore, this research work introduces facial emotion recognition of an instructor in a learning environment with the help of Evolving Hypergraph Manta ray Foraging Convolution Neural Network (EHMFCNN). Initially, the collected lecture videos are converted into frames, and the feature extraction is performed by fast discrete curvelet with wrapping technique. Then, to perform an effective recognition process, the frames are classified into multimedia representation, code walkthrough sessions and instructor presence in the lecture using dense residual dual-shuffle attention network. After classification, the instructor-based frames are selected for further recognition process. Then, evolving hypergraph convolution neural network is utilized to recognize the emotions of the instructor, and manta ray foraging optimization algorithm is employed for weight tuning process. Finally, the proposed system provides appropriate feedback for instructors using recognized emotions. The EHMFCNN system is tested on NPTEL-Indian English speech dataset, and it achieves highest accuracy of 97.5%, which is better than conventional classifiers. Extensive research determines that the introduced scheme improves the performance of the instructor emotion recognition system. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) are essentially applicable for feature extraction of images due to their robust mapping ability. However, due to the locality of CNNs, most of the existing methods that utilize CNNs for facial expression recognition (FER) cannot be well-suited for face images with occlusions or pose variations in the wild. Although our previous attention-modulated contextual spatial information network (ACSI-Net) is effective for FER, the loss function in ACSI-Net is not able to sufficiently constrain the network for the perception of discriminative facial features. In this paper, a novel constraining function called joint loss, tailored for FER in the wild, is offered to supervise ACSI-Net learning. In the Taylor expansion of the cross-entropy loss, the relative weights of the polynomial basis are adjusted to increase the learning intensity for categories with fewer samples, which produces our equilibrium loss. In addition, the regularization term is defined as the Euclidean distance between features and their class centers, with attention masking, which is named sparse center loss. Benefiting from the combination of equilibrium loss and sparse center loss, the joint loss can explore features with inter-class separateness and intra-class compactness from unbalanced data, leading to improved recognition performance. Experiments demonstrate that the presented joint loss, constraining ACSI-Net, achieves superior performance to ACSI-Net and is competitive with relevant advanced methods on two benchmark datasets, RAF_DB and AffectNet. [ABSTRACT FROM AUTHOR]

Existing facial expression recognition (FER) methods are mainly devoted to learning discriminative features from normal-light images. However, their performance drops sharply when they are used for low-light images. In this paper, we propose a novel low-light FER framework (termed LL-FER) that can simultaneously enhance the images and recognition tasks of low-light facial expression images. Specifically, we first meticulously design a low-light enhancement network (LLENet) to recover expressions images’ rich detail information. Then, we design a joint loss to train the LLENet with FER network in a cascade manner, so that the FER network can guide the LLENet to gradually perceive and restore discriminative features which are useful for FER during the training process. Extensive experiments show that the LLENet not only achieves competitive results both quantitatively and qualitatively, but also in the LL-FER framework, which can produce results more suitable for FER tasks, further improving the performance of the FER methods. [ABSTRACT FROM AUTHOR]

Face expression recognition in the wild faces challenges such as small data size, low quality images, and noisy labels. In order to solve these problems, this paper proposes a novel structural self-contrast learning method (SSCL) based on adaptively weighted negative samples. Firstly, two augmented variants are generated for each input sample as positive samples, which should be as similar in structure as possible. Secondly, EMD (Earth's Mover Distance) is used to define the structural similarity between the two augmented samples and extract the features in a self-supervised way. Subsequently, the concept of negative samples is further introduced into SSCL, and the augmented variants of other categories are generated as negative samples, which makes the structural similarity between positive samples increase and between negative samples simultaneously decrease. Finally, an adaptive weighting method for the negative is proposed, which aims to solve the imbalance between positive samples and negative samples, mine difficult negative samples, and reduce their similarity with the input image in an adaptive way. The extensive experiments on benchmark datasets demonstrate that SSCL outperforms the state-of-the-art methods. The cross-dataset evaluation shows the superior generalization capability of the proposed method. The code will be available at https://github.com/zjhzhy/SSCL. [ABSTRACT FROM AUTHOR]

In the field of facial expression recognition (FER), two main trends point to the data-driven FER and feature-driven FER exist. The former focused on the data problems (e.g., sample imbalance and multimodal fusion), while the latter explored the facial expression features. As the feature-driven FER is more important than the data-driven FER, for deeper mining of facial features, we propose an expression recognition model based on Local–Global information Reasoning and Landmark Spatial Distributions. Particularly to reason local–global information, multiple attention mechanisms with the modified residual module are designed for the Res18-LG module. In addition, taking the spatial topology of facial landmarks into account, a topological relationship graph of landmarks and a two-layer graph neural network are introduced to extract spatial distribution features. Finally, the experiment results on FERPlus and RAF-DB datasets demonstrate that our model outperforms the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Facial expression recognition (FER) is a tedious task in image processing for complex real-world scenarios that are captured under different lighting conditions, facial obstructions, and a diverse range of facial orientations. To address this issue, a novel Twinned attention network (Twinned-Att) is proposed in this paper for an efficient FER in occluded images. The proposed Twinned-Att network is designed in two separate modules: Holistic module (HM) and landmark centric module (LCM). The holistic module comprises of dual coordinate attention block (Dual-CA) and the Cross Convolution block (Cross-conv). The Dual-CA block is essential for learning positional, spatial, and contextual information by highlighting the most prominent characteristics in the facial regions. The Cross-conv block learns the spatial inter-dependencies and correlations to identify complex relationships between various facial regions. The LCM emphasizes smaller and distinct local regions while maintaining resilience against occlusions. Vigorous experiments have been undertaken to improve the efficacy of the proposed Twinned-Att. The results produced by the Twinned-Att illustrate the remarkable responses which achieve the accuracies of 86.92%, 85.64%, 78.40%, 69.82%, 64.71%, 85.52%, and 85.83% for the datasets viz., RAF DB, FER PLUS, FER 2013, FED RO, SFEW 2.0, occluded RAF DB and occluded FER Plus respectively. The proposed Twinned-Att network is experimented with various backbone networks, including Resnet-18, Resnet-50, and Resnet-152. It consistently outperforms well and highlights its prowess in addressing the challenges of robust FER in the images captured in complex real-world environments. [ABSTRACT FROM AUTHOR]

This work introduces the representation ensemble learning algorithm, a novel approach for generating diverse unsupervised representations rooted in the principles of self-taught learning. The ensemble comprises convolutional autoencoders (CAEs) learned in an unsupervised manner, fostering diversity via a loss function designed to penalize similar CAEs' latent representations. We employ support vector machines, bagging, and random forest as primary classification methods for the final classification step. Additionally, we incorporate KnoraU, a well-established technique used to dynamically select competent classifiers based on a test sample. We evaluate various fusion strategies, including sum, product, and stacking, to comprehensively assess the ensemble's performance. A robust experimental protocol considering the facial expression recognition problem shows that the proposed approach based on self-taught learning surpasses the accuracy of fine-tuned convolutional neural network (CNN) models. In terms of accuracy, the proposed method is up to 9.9 and 6.3 percentage points better than the CNN-based models fine-tuned for JAFFE and CK+ datasets, respectively. [ABSTRACT FROM AUTHOR]

Noisy labels are unavoidable in facial expression recognition (FER) task, significantly hindering FER performance in real-world scenarios. Recent advances tackle this problem by leveraging uncertainty for sample partitioning or constructing label distributions. However, these approaches primarily depend on labels, leading to confirmation bias issues and performance degradation. We argue that mining both label-independent features and label-dependent information can mitigate the confirmation bias induced by noisy labels. In this paper, we propose MCR, that is, mining simple yet effective label-free consistency regularization (MCR) to learn robust representations against noisy labels. The proposed MCR incorporates three label-free consistency regularizations: instance-level embedding consistency regularization, pairwise distance consistency regularization, and neighbour consistency regularization. Initially, we employ instance-level embedding consistency regularization to learn instance-level discriminative information from identical facial samples under perturbations in an unsupervised manner. This facilitates the efficacy of mitigating inherent noise in data. Subsequently, a pairwise distance consistency regularization is constructed to regularize the classifier and alleviate bias induced by noisy labels. Finally, we use the neighbour consistency regularization to further strengthen the discriminative capability of the model against noise. Benefiting from the advantages of these three label-free consistency regularizations, MCR can learn discriminative and robust representations against noise. Extensive experimental results demonstrate the superior performance of MCR on three popular in-the-wild facial expression datasets, including RAF-DB, FERPlus, and AffectNet. Moreover, MCR demonstrates superior generalization capability on other datasets with noisy labels, such as CIFAR100 and Tiny-ImageNet. [ABSTRACT FROM AUTHOR]

By recognizing students’ facial expressions in actual classroom situations, the students’ emotional states can be quickly uncovered, which can help teachers grasp the students’ learning rate, which allows teachers to adjust their teaching strategies and methods, thus improving the quality and effectiveness of classroom teaching. However, most previous facial expression recognition methods have problems such as missing key facial features and imbalanced class distributions in the dateset, resulting in low recognition accuracy. To address these challenges, this paper proposes LCANet, a model founded on a fused attention mechanism and a joint loss function, which allows the recognition of students’ emotions in real classroom scenarios. The model uses ConvNeXt V2 as the backbone network to optimize the global feature extraction capability of the model, and at the same time, it enables the model to pay closer attention to the key regions in facial expressions. We incorporate an improved Channel Spatial Attention (CSA) module as a way to extract more local feature information. Furthermore, to mitigate the class distribution imbalance problem in the facial expression dataset, we introduce a joint loss function. The experimental results show that our LCANet model has good recognition rates on both the public emotion datasets FERPlus, RAF-DB and AffectNet, with accuracies of 91.43%, 90.03% and 64.43%, respectively, with good robustness and generalizability. Additionally, we conducted experiments using the model in real classroom scenarios, detecting and accurately predicting students’ classroom emotions in real time, which provides an important reference for improving teaching in smart teaching scenarios. [ABSTRACT FROM AUTHOR]

The cognitive emotions of individuals during tasks largely determine the success or failure of tasks in various fields such as the military, medical, industrial fields, etc. Facial video data can carry more emotional information than static images because emotional expression is a temporal process. Video-based Facial Expression Recognition (FER) has received increasing attention from the relevant scholars in recent years. However, due to the high cost of marking and training video samples, feature extraction is inefficient and ineffective, which leads to a low accuracy and poor real-time performance. In this paper, a cognitive emotion recognition method based on video data is proposed, in which 49 emotion description points were initially defined, and the spatial–temporal features of cognitive emotions were extracted from the video data through a feature extraction method that combines geodesic distances and sample entropy. Then, an active learning algorithm based on complexity and uncertainty was proposed to automatically select the most valuable samples, thereby reducing the cost of sample labeling and model training. Finally, the effectiveness, superiority, and real-time performance of the proposed method were verified utilizing the MMI Facial Expression Database and some real-time-collected data. Through comparisons and testing, the proposed method showed satisfactory real-time performance and a higher accuracy, which can effectively support the development of a real-time monitoring system for cognitive emotions. [ABSTRACT FROM AUTHOR]

Facial expression recognition (FER) is significant for daily mental health monitoring because facial expressions reflect an individual's mental condition. However, it is still hard to achieve accurate and convenient FER using wearable devices. Here, a high‐accuracy, self‐powered, and intelligent FER system is reported consisting of a triboelectric hydrogel sensor network to collect facial expression signals and a deep learning model to process and recognize the signals. The triboelectric hydrogel sensors are demonstrated to show excellent properties, such as 50% stretchability, 90% transparency, and a response time of 48 ms. With a 1D convolutional neural network, six basic expressions can be recognized with an average recognition accuracy of 99.44%. Finally, a 3D virtual character model is built on a computer to display real emotions synchronously. Compared with previous similar reports, this system can recognize more types of expressions with significantly improved accuracy. Therefore, this work can potentially not only help doctors to determine a patient's mental health condition through virtual communication while protecting the patient's privacy but also provide a highly promising approach for virtual telemedicine in the future. [ABSTRACT FROM AUTHOR]

People routinely use facial expressions to communicate successfully and to regulate other’s behaviour, yet modelling the form and meaning of these facial behaviours has proven surprisingly complex. One reason for this difficulty may lie in an over-reliance on the assumptions inherent in existing theories of facial expression – specifically that (1) there is a putative set of facial expressions that signal an internal state of emotion, (2) patterns of facial movement have been empirically linked to the prototypical emotions in this set, and (3) static, non-social, posed images from convenience samples are adequate to validate the first two assumptions. These assumptions have guided the creation of datasets, which are then used to train unrepresentative computational models of facial expression. In this article, we discuss existing theories of facial expression and review how they have shaped current facial expression recognition tools. We then discuss the resources that are available to help researchers build a more ecologically valid model of facial expressions. [ABSTRACT FROM AUTHOR]

Facial expression recognition (FER) is one of the important research topics in computer vision. It is difficult to obtain high accuracy in FER tasks, especially when the high-quality labeled data are insufficient. Indeed, the facial images with non-frontal faces, occlusions and inaccurate labels heavily affects the training results of FER network models, which causes low recognition accuracy and poor robustness. To this end, we propose a novel strategy for FER tasks through combining transfer learning and generative adversarial network (GAN). First, we enlarge the training datasets by introducing an effective face-cycle GAN to synthesize additional facial expression images. Then, we develop two FER neural networks based on two representative convolutional neural networks (CNN). By transferring the cross-domain knowledge from the two well-trained CNNs to the proposed FER CNNs, it not only obtains more pre-trained knowledge and also accelerates the training process greatly. The experimental results show that the proposed FER CNNs integrated with the new face-cycle GAN achieves high accuracies 98.44%, 95.24% and 91.67% on three widely used datasets CK + , JAFFE, and Oulu-CASIA, respectively. Compared to the results obtained by other state-of-the-art FER methods, the accuracies are improved by 0.34%, 0.24%, and 2.62%, respectively. [ABSTRACT FROM AUTHOR]

This study enhances the generalization ability and recognition accuracy of convolutional neural networks (CNNs) in educational settings, particularly in the task of Facial Expression Recognition (FER), to support effective analysis of classroom teaching behaviors. A novel multimodal teaching behavior analysis model is proposed to achieve this goal, combining the Adaptive Deep Convolutional Neural Network (ADCNN), an Adaptive Entropy Minimization algorithm, and Transfer Learning. The aim is to improve teaching quality through more precise behavior recognition. A Bayesian Network based on a Hybrid Deep Restricted Boltzmann Machine is introduced to enhance the model's performance, enabling effective processing and analysis of multimodal data. The model's performance in the FER task has been extensively validated through experiments, encompassing precise analysis of training and testing data. These methods allow the model to adapt to different datasets automatically and improve overall performance. In addition, the study collects data on classroom engagement and learning interest from Chinese university students through questionnaires, further supporting the model's application in real educational environments. Experimental results show that the proposed model performs exceptionally well in the FER task, with an average training precision of approximately 99.79% and a testing precision of about 99.60%. The maximum difference between training and testing precision is 0.19%. The questionnaire results reveal issues such as low English proficiency and insufficient classroom engagement among students at Heilongjiang Bayi Agricultural University, providing data to support the improvement of teaching methods. The proposed multimodal teaching behavior analysis model provides an effective tool for optimizing classroom teaching by accurately recognizing students' facial expressions and classroom behaviors. [ABSTRACT FROM AUTHOR]

Previous studies found that exposure to self-facial expressions (SFE) facilitated recognition of other-facial expressions (OFE) in typical individuals. However, this effect is unclear for individuals with autism spectrum disorder (ASD). This study designed an SFE recognition training curriculum and employed a nonconcurrent multiple probe-baseline design across participants to assess the effects of training on SFE recognition directly and OFE recognition indirectly in three low-functioning children with ASD aged 8-9 years. With the gradual improvement of SFE recognition during training, the performance of children with ASD in OFE recognition increased slightly. In addition, the training and maintenance effects of SFE and OFE recognition were strong across participants. This result suggested that SFE recognition training was beneficial for OFE recognition in children with ASD and should be included in OFE training programs in the future. [ABSTRACT FROM AUTHOR]

Visual biosignals can be used to analyze human behavioral activities and serve as a primary resource for Facial Expression Recognition (FER). FER computational systems face significant challenges, arising from both spatial and temporal effects. Spatial challenges include deformations or occlusions of facial geometry, while temporal challenges involve discontinuities in motion observation due to high variability in poses and dynamic conditions such as rotation and translation. To enhance the analytical precision and validation reliability of FER systems, several datasets have been proposed. However, most of these datasets focus primarily on spatial characteristics, rely on static images, or consist of short videos captured in highly controlled environments. These constraints significantly reduce the applicability of such systems in real-world scenarios. This paper proposes the Facial Biosignals Time–Series Dataset (FBioT), a novel dataset providing temporal descriptors and features extracted from common videos recorded in uncontrolled environments. To automate dataset construction, we propose Visual–Temporal Facial Expression Recognition (VT-FER), a method that stabilizes temporal effects using normalized measurements based on the principles of the Facial Action Coding System (FACS) and generates signature patterns of expression movements for correlation with real-world temporal events. To demonstrate feasibility, we applied the method to create a pilot version of the FBioT dataset. This pilot resulted in approximately 10,000 s of public videos captured under real-world facial motion conditions, from which we extracted 22 direct and virtual metrics representing facial muscle deformations. During this process, we preliminarily labeled and qualified 3046 temporal events representing two emotion classes. As a proof of concept, these emotion classes were used as input for training neural networks, with results summarized in this paper and available in an open-source online repository. [ABSTRACT FROM AUTHOR]

Single‐mode sensors suffer from poor robustness and insufficient data features in facial expression recognition, so fusing multi‐sensor signals is the key to improving the accuracy of expression recognition systems. Here, a biocompatible capacitive‐electromyographic dual‐mode sensor (CEDS) is presented, consisting of a capacitive pressure sensing unit and dry electrodes for electrophysiological signal monitoring, assembled in a 3D stacking fashion. A double‐coupled microstructure is prepared and the electrical double‐layer effect is realized by doping ionic liquid, which significantly improves the capacitive performance of the sensor. The application of dry electrodes effectively solves the problems of hydrogel electrodes that are prone to water loss and skin irritation. Besides, the good biocompatibility and antimicrobial properties of CEDS are verified through cytotoxicity and bacteriostatic tests. Based on the sensing of a single signal, a fatigue driving monitoring system and a manipulator control system are constructed respectively. By further integrating the capacitive and electrophysiological signal monitoring functions of CEDS, a 1D convolutional neural network‐assisted facial expression recognition system is constructed, which effectively improves the accuracy of expression recognition and demonstrates the great potential of facial expression monitoring systems based on flexible sensor technology in practical applications. [ABSTRACT FROM AUTHOR]

The rapid expansion of artificial intelligence technologies has enabled machines to comprehend emotional intelligence. Among various indicators, facial expressions serve as an effective medium for understanding emotions. The concept of facial expression recognition (FER) relies heavily on the accurate and robust features available. Initially, the method of three-channel convolutional neural networks (TC-CNN) is adapted to extract facial features. However, only extracting the features is insufficient, the optimization of the extracted features is crucial to determining precise and robust features. This research work focuses on the optimization of the features using the quantum-inspired vortex search algorithm (QVSA). The QVSA integrates the attributes of Q-bits into the vortex search algorithm (VSA), optimizing the features by using the Q-bits to determine the vortex center on the Bloch sphere. The Q-bit attributes also improve the diversity of the features and help to avoid the premature convergence of the VSA. The final recognition of the facial expressions is performed using the deep neural network method of ResNet101v2. The experiments for facial expression recognition are performed on the datasets of RaFD and KDEF, which include different facial positions such as front pose, diagonal pose and profile pose. Performance comparisons demonstrate the effectiveness of the proposed system over state-of-the-art facial expression techniques. [ABSTRACT FROM AUTHOR]

Recognition of facial expressions is a challenging task in computer vision because of the complexity associated with individual facial features and social differences. Early studies classified human facial expressions into six basic categories which are anger, disgust, fear, happiness, sadness and surprise. The neutral expression is also taken into account. Furthermore, compound emotions are explored on human faces which are the representations of the expressions that entail the combination of more than a single basic facial expression. Including at least two expression categories, one is considered as the dominating expression and the other as the complementary expression. In this way, the categorization of compound facial expressions is done. In this study, a novel approach is proposed to recognize compound facial expressions. The main contribution of this paper is the proposed fusion of deep texture and geometric features. The texture features are the deep textures obtained from a deep learning model. The iCV-MEFED dataset is employed. It includes compound facial expressions consisting of all the combinations of basic facial expressions in the sense of dominating and complementary expressions. Therefore, 50 distinct classes of facial expressions are presented. The previous studies carried out on this dataset report high rates of misclassification due to the challenge of the complexity of facial expressions and correlations among the compound expressions. The proposed approach obtained encouraging results and has shown significant improvements in the recognition accuracy of compound facial expressions on the iCV-MEFED dataset. [ABSTRACT FROM AUTHOR]

A customer information system (CIS) is a crucial component of a customer relationship management (CRM) system. The CIS collects, stores, and manages customer data to enhance the customer experience and sales. Deep learning technology has been used to develop facial expression recognition systems that can accurately identify and analyze facial expressions. By integrating facial expression recognition deep learning into a CIS for CRM, businesses can gain a deeper understanding of the emotions and behavior of the customers, allowing them to provide more personalized experiences. The data collected by the CIS can then be used to personalize marketing campaigns, tailor product recommendations, and improve customer service interactions. The integration of facial expression recognition deep learning into a CIS for CRM has the potential to revolutionize the way businesses interact with their customers. This paper constructed a Feature Fusion Deep Multi-Layer Classification (FFDMLC) model for facial expression recognition for the CIS system. With FFDMLC model comprises the feature-fusion model in face recognition. The propsoed FFDMLC approach fuses various features and estimates the feature fusion model. The FFDMLC model employs deep learning for feature computation and expression classification in individuals. The hyperparameters of this proposed method are optimized using the COOT optimization method. This process optimizes reaction conditions, leading to increased yields, minimized by-products, and improved overall efficiency. The CIS model uses the facial expression recognition system for the computation of facial features in the video sequences. The proposed FFDMLC model is evaluated based on the consideration of two datasets such asCK + and the FER2013 dataset, which also exhibits a higher recognition rate of 99.93% for the understanding opinion of the customers. [ABSTRACT FROM AUTHOR]

There has been a lot of progress in recent years in the fields of expert systems, artificial intelligence (AI) and human machine interface (HMI). The use of voice commands to engage with machinery or instruct it to do a certain task is becoming more common. Numerous consumer electronics have SIRI, Alexa, Cortana, and Google Assistant built in. In the field of human-device interaction, emotion recognition from speech is a complex research subject. We can't imagine modern life without machines, so naturally there's a need to create a more robust framework for humanmachine communication. A number of academics are now working on speech emotion recognition (SER) in an effort to improve the interaction between humans and machines. We aimed to identify four fundamental emotions: angry, unhappy, neutral and joyful from speech in our experiment. As you can hear below, we trained and tested our model using audio data of brief Manipuri speeches taken from films. This task makes use of convolutional neural networks (CNNs) to extract functions from speech in order to recognize different moods using the Mel-frequency cepstral coefficient (MFCC). [ABSTRACT FROM AUTHOR]

Potential uses of automated Facial Expression Recognition (FER) cover a wide range of applications such as customer behavior analysis, healthcare applications or providing personalized services. Data for machine learning play a fundamental role, therefore, understanding the relevancy of the data in the outcomes is of utmost importance. In this work we present a study on how gender influences the learning of a FER system. We analyze with Explainable Artificial intelligence (XAI) techniques how gender contributes to the learning and assess which facial expressions are more similar regarding face regions that impact on the classification. Results show that there exist common regions in some expressions both for females and males with different intensities (e.g. happiness); however, there are other expressions like disgust, where important face regions differ. The insights of this work will help improving FER systems and understand the source of any inequality. [ABSTRACT FROM AUTHOR]