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Dynamic Facial Expression Recognition of Learners via Adaptive Global Attention and Differential Temporal Transformer 认领 引用
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作者 Wei Liu Lujia Li +4 位作者 Chun Yan Yulin Zhang Xiaochun Cheng Xinyan Zhao Mingshi Liu 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2026年第2期514-528,共15页
Analysing learners'facial expressions during learning and exploring their learning processes and emotional changes are of great significance for assisting teachers'teaching and promoting smart education.In com... Analysing learners'facial expressions during learning and exploring their learning processes and emotional changes are of great significance for assisting teachers'teaching and promoting smart education.In complex learning environments,static facial expression recognition fails to capture the dynamic changes of learners'expressions losing the continuous features in the learning process,and its recognition effect is easily interfered with by factors such as occlusion and lighting variations during learning.To address the above issues,a network model based on adaptive global attention and temporal difference is proposed to recognise learners'dynamic expression sequences.Firstly,we have designed an Adaptive Global Attention(AGA)block,which adaptively models inter-channel relationships to dynamically enhance key channels that are highly correlated with learners'states while suppressing redundant information,thereby improving the model's feature representation capability under noisy environments.Secondly,we have designed a Differential Temporal Transformer(DTFormer)to extract differential information between consecutive frames,increasing the model's sensitivity to learners'facial expression dynamics and improving recognition performance.The two components complement each other in terms of spatial feature enhancement and temporal dynamic modelling effectively improving the model's overall capability for representing learners'dynamic facial expressions.Experiments were conducted on public datasets DFEW,FERV39k and the learner E-learning emotional state data set DAiSEE,and comparisons were made with classical methods using objective indicators.The results demonstrate that the proposed method outperforms the comparison methods in multiple performance indicators,thereby verifying its effectiveness. 展开更多
关键词 face analysis facial expression recognition spatial-temporal feature transformer
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SQSNet: Hybrid CNN-Transformer Fusion with Spatial Quad-Similarity for Robust Facial Expression Recognition 认领 引用
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作者 Mohammed AAhmed Jian Dong +3 位作者 Ronghua Shi Ammar Nassr Hani Almaqtari Ala AAlsanabani 《Computers, Materials & Continua》 SCIE EI 2026年第6期1781-1800,共20页
Facial Expression Recognition(FER)is an essential endeavor in computer vision,applicable in human-computer interaction,emotion assessment,and mental health surveillance.Although Convolutional Neural Networks(CNNs)have... Facial Expression Recognition(FER)is an essential endeavor in computer vision,applicable in human-computer interaction,emotion assessment,and mental health surveillance.Although Convolutional Neural Networks(CNNs)have proven effective in Facial Emotion Recognition,they encounter difficulties in capturing long-range connections and global context.To address these constraints,we propose Spatial Quad-Similarity Network(SQSNet),an innovative hybrid framework that integrates the local feature extraction capabilities of CNNs with the global contextual modeling efficacy of Swin Transformers via a cohesive fusion technique.SQSNet introduces the Spatial Quad-Similarity(SQS)module,a feature refinement approach that amplifies discriminative characteristics and mitigates redundancy.Unlike conventional metric learning approaches that operate on global feature representations,SQS computes fine-grained spatial-level similarity across multiple instances,enforcing H×W independent constraints that preserve spatial correspondence between expression-relevant facial regions.This spatial-level formulation is particularly effective for FER,where expressions manifest as localized muscle movements that are lost in global pooling operations.Moreover,SQSNet employs sophisticated regularization methods,including Mixup augmentation,label smoothing,and adaptive learning rate scheduling,to enhance generalization.Experimental findings on three benchmark datasets,RAF-DB,FERPlus,and AffectNet,indicate that SQSNet surpasses current FER methodologies,attaining state-of-the-art accuracies of 91.90%,91.11%,and 67.15%,respectively.These findings underscore the efficacy of integrating CNNs,Swin Transformers,and spatial similarity-driven feature refining for facial emotion identification,facilitating the development of more dependable emotion recognition systems. 展开更多
关键词 Facial expression recognition convolutional neural networks swin transformers cross attention adaptive learning
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Real-Time Facial Expression Recognition on Res-MobileNetV3 认领 引用 被引量：4
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作者 Li Beibei Zhu Jiansheng +3 位作者 Li Suwen Dai Linlin Yan Zhiyuan Ma Liangde 《China Communications》 SCIE EI CSCD 2025年第3期54-64,共11页
Artificial intelligence,such as deep learning technology,has advanced the study of facial expression recognition since facial expression carries rich emotional information and is significant for many naturalistic situ... Artificial intelligence,such as deep learning technology,has advanced the study of facial expression recognition since facial expression carries rich emotional information and is significant for many naturalistic situations.To pursue a high facial expression recognition accuracy,the network model of deep learning is generally designed to be very deep while the model’s real-time performance is typically constrained and limited.With MobileNetV3,a lightweight model with a good accuracy,a further study is conducted by adding a basic ResNet module to each of its existing modules and an SSH(Single Stage Headless Face Detector)context module to expand the model’s perceptual field.In this article,the enhanced model named Res-MobileNetV3,could alleviate the subpar of real-time performance and compress the size of large network models,which can process information at a rate of up to 33 frames per second.Although the improved model has been verified to be slightly inferior to the current state-of-the-art method in aspect of accuracy rate on the publically available face expression datasets,it can bring a good balance on accuracy,real-time performance,model size and model complexity in practical applications. 展开更多
关键词 artificial intelligence facial expression recognition MobileNetV3 ResNet SSH
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Face Expression Recognition on Uncertainty-Based Robust Sample Selection Strategy 认领 引用
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作者 Yuqi Wang Wei Jiang 《Journal of Electronic Research and Application》 2025年第2期211-215,共5页
In the task of Facial Expression Recognition(FER),data uncertainty has been a critical factor affecting performance,typically arising from the ambiguity of facial expressions,low-quality images,and the subjectivity of... In the task of Facial Expression Recognition(FER),data uncertainty has been a critical factor affecting performance,typically arising from the ambiguity of facial expressions,low-quality images,and the subjectivity of annotators.Tracking the training history reveals that misclassified samples often exhibit high confidence and excessive uncertainty in the early stages of training.To address this issue,we propose an uncertainty-based robust sample selection strategy,which combines confidence error with RandAugment to improve image diversity,effectively reducing overfitting caused by uncertain samples during deep learning model training.To validate the effectiveness of the proposed method,extensive experiments were conducted on FER public benchmarks.The accuracy obtained were 89.08%on RAF-DB,63.12%on AffectNet,and 88.73%on FERPlus. 展开更多
关键词 Facial expression recognition Uncertainty Sample selection strategy
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Identity-aware convolutional neural networks for facial expression recognition 认领 引用 被引量：14
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作者 Chongsheng Zhang Pengyou Wang +1 位作者 Ke Chen Joni-Kristian Kamarainen 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2017年第4期784-792,共9页
Facial expression recognition is a hot topic in computer vision, but it remains challenging due to the feature inconsistency caused by person-specific 'characteristics of facial expressions. To address such a chal... Facial expression recognition is a hot topic in computer vision, but it remains challenging due to the feature inconsistency caused by person-specific 'characteristics of facial expressions. To address such a challenge, and inspired by the recent success of deep identity network (DeepID-Net) for face identification, this paper proposes a novel deep learning based framework for recognising human expressions with facial images. Compared to the existing deep learning methods, our proposed framework, which is based on multi-scale global images and local facial patches, can significantly achieve a better performance on facial expression recognition. Finally, we verify the effectiveness of our proposed framework through experiments on the public benchmarking datasets JAFFE and extended Cohn-Kanade (CK+). 展开更多
关键词 facial expression recognition deep learning classification identity-aware
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Feature Representation for Facial Expression Recognition Based on FACS and LBP 认领 引用 被引量：15
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作者 Li Wang Rui-Feng Li +1 位作者 Ke Wang Jian Chen 《International Journal of Automation and computing》 CSCD 2014年第5期459-468,共10页
In expression recognition, feature representation is critical for successful recognition since it contains distinctive information of expressions. In this paper, a new approach for representing facial expression featu... In expression recognition, feature representation is critical for successful recognition since it contains distinctive information of expressions. In this paper, a new approach for representing facial expression features is proposed with its objective to describe features in an effective and efficient way in order to improve the recognition performance. The method combines the facial action coding system(FACS) and 'uniform' local binary patterns(LBP) to represent facial expression features from coarse to fine. The facial feature regions are extracted by active shape models(ASM) based on FACS to obtain the gray-level texture. Then, LBP is used to represent expression features for enhancing the discriminant. A facial expression recognition system is developed based on this feature extraction method by using K nearest neighborhood(K-NN) classifier to recognize facial expressions. Finally, experiments are carried out to evaluate this feature extraction method. The significance of removing the unrelated facial regions and enhancing the discrimination ability of expression features in the recognition process is indicated by the results, in addition to its convenience. 展开更多
关键词 Local binary patterns (LBP) facial expression recognition active shape models (ASM) facial action coding system (FACS) feature representation
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Facial Expression Recognition Model Depending on Optimized Support Vector Machine 认领 引用 被引量：5
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作者 Amel Ali Alhussan Fatma M.Talaat +4 位作者 El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Doaa Sami Khafaga Mona Alnaggar 《Computers, Materials & Continua》 SCIE EI 2023年第7期499-515,共17页
In computer vision,emotion recognition using facial expression images is considered an important research issue.Deep learning advances in recent years have aided in attaining improved results in this issue.According t... In computer vision,emotion recognition using facial expression images is considered an important research issue.Deep learning advances in recent years have aided in attaining improved results in this issue.According to recent studies,multiple facial expressions may be included in facial photographs representing a particular type of emotion.It is feasible and useful to convert face photos into collections of visual words and carry out global expression recognition.The main contribution of this paper is to propose a facial expression recognitionmodel(FERM)depending on an optimized Support Vector Machine(SVM).To test the performance of the proposed model(FERM),AffectNet is used.AffectNet uses 1250 emotion-related keywords in six different languages to search three major search engines and get over 1,000,000 facial photos online.The FERM is composed of three main phases:(i)the Data preparation phase,(ii)Applying grid search for optimization,and(iii)the categorization phase.Linear discriminant analysis(LDA)is used to categorize the data into eight labels(neutral,happy,sad,surprised,fear,disgust,angry,and contempt).Due to using LDA,the performance of categorization via SVM has been obviously enhanced.Grid search is used to find the optimal values for hyperparameters of SVM(C and gamma).The proposed optimized SVM algorithm has achieved an accuracy of 99%and a 98%F1 score. 展开更多
关键词 Facial expression recognition machine learning linear dis-criminant analysis(LDA) support vector machine(SVM) grid search
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Brain functional changes in facial expression recognition in patients with major depressive disorder before and after antidepressant treatment A functional magnetic resonance imaging study 认领 引用 被引量：3
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作者 Wenyan Jiang Zhongmin Yint +3 位作者 Yixin Pang Feng Wu Lingtao Kong Ke Xu 《Neural Regeneration Research》 SCIE CAS CSCD 2012年第15期1151-1157,共7页
Functional magnetic resonance imaging was used during emotion recognition to identify changes in functional brain activation in 21 first-episode, treatment-naive major depressive disorder patients before and after ant... Functional magnetic resonance imaging was used during emotion recognition to identify changes in functional brain activation in 21 first-episode, treatment-naive major depressive disorder patients before and after antidepressant treatment. Following escitalopram oxalate treatment, patients exhibited decreased activation in bilateral precentral gyrus, bilateral middle frontal gyrus, left middle temporal gyrus, bilateral postcentral gyrus, left cingulate and right parahippocampal gyrus, and increased activation in right superior frontal gyrus, bilateral superior parietal Iobule and left occipital gyrus during sad facial expression recognition. After antidepressant treatment, patients also exhibited decreased activation in the bilateral middle frontal gyrus, bilateral cingulate and right parahippocampal gyrus, and increased activation in the right inferior frontal gyrus, left fusiform gyrus and right precuneus during happy facial expression recognition. Our experimental findings indicate that the limbic-cortical network might be a key target region for antidepressant treatment in major depressive disorder. 展开更多
关键词 major depressive disorder functional magnetic resonance imaging facial expression recognition antidepressant neural regeneration
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Facial expression recognition using threestage support vector machines 认领 引用 被引量：3
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作者 Issam Dagher Elio Dahdah Morshed Al Shakik 《Visual Computing for Industry,Biomedicine,and Art》 EI 2019年第1期236-244,共9页
Herein,a three-stage support vector machine(SVM)for facial expression recognition is proposed.The first stage comprises 21 SVMs,which are all the binary combinations of seven expressions.If one expression is dominant,... Herein,a three-stage support vector machine(SVM)for facial expression recognition is proposed.The first stage comprises 21 SVMs,which are all the binary combinations of seven expressions.If one expression is dominant,then the first stage will suffice;if two are dominant,then the second stage is used;and,if three are dominant,the third stage is used.These multilevel stages help reduce the possibility of experiencing an error as much as possible.Different image preprocessing stages are used to ensure that the features attained from the face detected have a meaningful and proper contribution to the classification stage.Facial expressions are created as a result of muscle movements on the face.These subtle movements are detected by the histogram-oriented gradient feature,because it is sensitive to the shapes of objects.The features attained are then used to train the three-stage SVM.Two different validation methods were used:the leave-one-out and K-fold tests.Experimental results on three databases(Japanese Female Facial Expression,Extended Cohn-Kanade Dataset,and Radboud Faces Database)show that the proposed system is competitive and has better performance compared with other works. 展开更多
关键词 Facial expression recognition Support vector machine Histogram of oriented gradients Viola-Jones Validation
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Person-independent expression recognition based on person-similarity weighted expression feature 认领 引用 被引量：2
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作者 Huachun Tan Yujin Zhang +2 位作者 Hao Chen Yanan Zhao Wuhong Wang 《Journal of Systems Engineering and Electronics》 SCIE EI 2010年第1期118-126,共9页
A new method to extract person-independent expression feature based on higher-order singular value decomposition （HOSVD） is proposed for facial expression recognition. Based on the assumption that similar persons ha... A new method to extract person-independent expression feature based on higher-order singular value decomposition （HOSVD） is proposed for facial expression recognition. Based on the assumption that similar persons have similar facial expression appearance and shape, the person-similarity weighted expression feature is proposed to estimate the expression feature of test persons. As a result, the estimated expression feature can reduce the influence of individuals caused by insufficient training data, and hence become less person-dependent. The proposed method is tested on Cohn-Kanade facial expression database and Japanese female facial expression （JAFFE） database. Person-independent experimental results show the superiority of the proposed method over the existing methods. 展开更多
关键词 facial expression recognition person-independent ex-pression feature higher-order singular value decomposition feature estimation.
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The deep spatiotemporal network with dual-flow fusion for video-oriented facial expression recognition 认领 引用 被引量：1
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作者 Chenquan Gan Jinhui Yao +2 位作者 Shuaiying Ma Zufan Zhang Lianxiang Zhu 《Digital Communications and Networks》 SCIE CSCD 2023年第6期1441-1447,共7页
The video-oriented facial expression recognition has always been an important issue in emotion perception.At present,the key challenge in most existing methods is how to effectively extract robust features to characte... The video-oriented facial expression recognition has always been an important issue in emotion perception.At present,the key challenge in most existing methods is how to effectively extract robust features to characterize facial appearance and geometry changes caused by facial motions.On this basis,the video in this paper is divided into multiple segments,each of which is simultaneously described by optical flow and facial landmark trajectory.To deeply delve the emotional information of these two representations,we propose a Deep Spatiotemporal Network with Dual-flow Fusion(defined as DSN-DF),which highlights the region and strength of expressions by spatiotemporal appearance features and the speed of change by spatiotemporal geometry features.Finally,experiments are implemented on CKþand MMI datasets to demonstrate the superiority of the proposed method. 展开更多
关键词 Facial expression recognition Deep spatiotemporal network Optical flow Facial landmark trajectory Dual-flow fusion
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Facial Expression Recognition Based on Multi-Channel Attention Residual Network 认领 引用 被引量：3
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作者 Tongping Shen Huanqing Xu 《Computer Modeling in Engineering & Sciences》 SCIE EI 2023年第4期539-560,共22页
For the problems of complex model structure and too many training parameters in facial expression recognition algorithms,we proposed a residual network structure with a multi-headed channel attention(MCA)module.The mi... For the problems of complex model structure and too many training parameters in facial expression recognition algorithms,we proposed a residual network structure with a multi-headed channel attention(MCA)module.The migration learning algorithm is used to pre-train the convolutional layer parameters and mitigate the overfitting caused by the insufficient number of training samples.The designed MCA module is integrated into the ResNet18 backbone network.The attention mechanism highlights important information and suppresses irrelevant information by assigning different coefficients or weights,and the multi-head structure focuses more on the local features of the pictures,which improves the efficiency of facial expression recognition.Experimental results demonstrate that the model proposed in this paper achieves excellent recognition results in Fer2013,CK+and Jaffe datasets,with accuracy rates of 72.7%,98.8%and 93.33%,respectively. 展开更多
关键词 Facial expression recognition channel attention ResNet18 dataset
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Facial Expression Recognition Based on the Q-shift DT-CWT and Rotation Invariant LBP 认领 引用 被引量：2
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作者 陈蕾 王加俊 孙兵 《Journal of Donghua University(English Edition)》 EI CAS 2012年第1期71-75,共5页
In this paper, a novel method based on dual-tree complex wavelet transform(DT-CWT) and rotation invariant local binary pattern(LBP) for facial expression recognition is proposed. The quarter sample shift (Q-shift) DT-... In this paper, a novel method based on dual-tree complex wavelet transform(DT-CWT) and rotation invariant local binary pattern(LBP) for facial expression recognition is proposed. The quarter sample shift (Q-shift) DT-CWT can provide a group delay of 1/4 of a sample period, and satisfy the usual 2-band filter bank constraints of no aliasing and perfect reconstruction. To resolve illumination variation in expression verification, low-frequency coefficients produced by DT-CWT are set zeroes, high-frequency coefficients are used for reconstructing the image, and basic LBP histogram is mapped on the reconstructed image by means of histogram specification. LBP is capable of encoding texture and shape information of the preprocessed images. The histogram graphs built from multi-scale rotation invariant LBPs are combined to serve as feature for further recognition. Template matching is adopted to classify facial expressions for its simplicity. The experimental results show that the proposed approach has good performance in efficiency and accuracy. 展开更多
关键词 facial expression recognition dual-tree complex wavelet transform (DT-CWT) local binary pattern(LBP) histogram similarity measure
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Facial expression recognition based on fuzzy-LDA/CCA 认领 引用 被引量：1
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作者 周晓彦 郑文明 +1 位作者 邹采荣 赵力 《Journal of Southeast University(English Edition)》 EI CAS 2008年第4期428-432,共5页
A novel fuzzy linear discriminant analysis method by the canonical correlation analysis （fuzzy-LDA/CCA）is presented and applied to the facial expression recognition. The fuzzy method is used to evaluate the degree o... A novel fuzzy linear discriminant analysis method by the canonical correlation analysis （fuzzy-LDA/CCA）is presented and applied to the facial expression recognition. The fuzzy method is used to evaluate the degree of the class membership to which each training sample belongs. CCA is then used to establish the relationship between each facial image and the corresponding class membership vector, and the class membership vector of a test image is estimated using this relationship. Moreover, the fuzzy-LDA/CCA method is also generalized to deal with nonlinear discriminant analysis problems via kernel method. The performance of the proposed method is demonstrated using real data. 展开更多
关键词 fuzzy linear discriminant analysis canonical correlation analysis facial expression recognition
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Expression Recognition Method Based on Convolutional Neural Network and Capsule Neural Network 认领 引用 被引量：1
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作者 Zhanfeng Wang Lisha Yao 《Computers, Materials & Continua》 SCIE EI 2024年第4期1659-1677,共19页
Convolutional neural networks struggle to accurately handle changes in angles and twists in the direction of images,which affects their ability to recognize patterns based on internal feature levels. In contrast, Caps... Convolutional neural networks struggle to accurately handle changes in angles and twists in the direction of images,which affects their ability to recognize patterns based on internal feature levels. In contrast, CapsNet overcomesthese limitations by vectorizing information through increased directionality and magnitude, ensuring that spatialinformation is not overlooked. Therefore, this study proposes a novel expression recognition technique calledCAPSULE-VGG, which combines the strengths of CapsNet and convolutional neural networks. By refining andintegrating features extracted by a convolutional neural network before introducing theminto CapsNet, ourmodelenhances facial recognition capabilities. Compared to traditional neural network models, our approach offersfaster training pace, improved convergence speed, and higher accuracy rates approaching stability. Experimentalresults demonstrate that our method achieves recognition rates of 74.14% for the FER2013 expression dataset and99.85% for the CK+ expression dataset. By contrasting these findings with those obtained using conventionalexpression recognition techniques and incorporating CapsNet’s advantages, we effectively address issues associatedwith convolutional neural networks while increasing expression identification accuracy. 展开更多
关键词 Expression recognition capsule neural network convolutional neural network
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E2E-MFERC:AMulti-Face Expression Recognition Model for Group Emotion Assessment 认领 引用
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作者 Lin Wang Juan Zhao +1 位作者 Hu Song Xiaolong Xu 《Computers, Materials & Continua》 SCIE EI 2024年第4期1105-1135,共31页
In smart classrooms, conducting multi-face expression recognition based on existing hardware devices to assessstudents’ group emotions can provide educators with more comprehensive and intuitive classroom effect anal... In smart classrooms, conducting multi-face expression recognition based on existing hardware devices to assessstudents’ group emotions can provide educators with more comprehensive and intuitive classroom effect analysis,thereby continuouslypromotingthe improvementof teaching quality.However,most existingmulti-face expressionrecognition methods adopt a multi-stage approach, with an overall complex process, poor real-time performance,and insufficient generalization ability. In addition, the existing facial expression datasets are mostly single faceimages, which are of low quality and lack specificity, also restricting the development of this research. This paperaims to propose an end-to-end high-performance multi-face expression recognition algorithm model suitable forsmart classrooms, construct a high-quality multi-face expression dataset to support algorithm research, and applythe model to group emotion assessment to expand its application value. To this end, we propose an end-to-endmulti-face expression recognition algorithm model for smart classrooms (E2E-MFERC). In order to provide highqualityand highly targeted data support for model research, we constructed a multi-face expression dataset inreal classrooms (MFED), containing 2,385 images and a total of 18,712 expression labels, collected from smartclassrooms. In constructing E2E-MFERC, by introducing Re-parameterization visual geometry group (RepVGG)block and symmetric positive definite convolution (SPD-Conv) modules to enhance representational capability;combined with the cross stage partial network fusion module optimized by attention mechanism (C2f_Attention),it strengthens the ability to extract key information;adopts asymptotic feature pyramid network (AFPN) featurefusion tailored to classroomscenes and optimizes the head prediction output size;achieves high-performance endto-end multi-face expression detection. Finally, we apply the model to smart classroom group emotion assessmentand provide design references for classroom effect analysis evaluation metrics. Experiments based on MFED showthat the mAP and F1-score of E2E-MFERC on classroom evaluation data reach 83.6% and 0.77, respectively,improving the mAP of same-scale You Only Look Once version 5 (YOLOv5) and You Only Look Once version8 (YOLOv8) by 6.8% and 2.5%, respectively, and the F1-score by 0.06 and 0.04, respectively. E2E-MFERC modelhas obvious advantages in both detection speed and accuracy, which can meet the practical needs of real-timemulti-face expression analysis in classrooms, and serve the application of teaching effect assessment very well. 展开更多
关键词 Multi-face expression recognition smart classroom end-to-end detection group emotion assessment
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Facial expression recognition with contextualized histograms 认领 引用
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作者 岳雷 沈庭芝 +2 位作者 杜部致 张超 赵三元 《Journal of Beijing Institute of Technology》 EI CAS 2015年第3期392-397,共6页
A new algorithm taking the spatial context of local features into account by utilizing contextualized histograms was proposed to recognize facial expression. The contextualized histograms were extracted fromtwo widely... A new algorithm taking the spatial context of local features into account by utilizing contextualized histograms was proposed to recognize facial expression. The contextualized histograms were extracted fromtwo widely used descriptors—the local binary pattern（ LBP） and weber local descriptor（ WLD）. The LBP and WLD feature histograms were extracted separately fromeach facial image,and contextualized histogram was generated as feature vectors to feed the classifier. In addition,the human face was divided into sub-blocks and each sub-block was assigned different weights by their different contributions to the intensity of facial expressions to improve the recognition rate. With the support vector machine（SVM） as classifier,the experimental results on the 2D texture images fromthe 3D-BU FE dataset indicated that contextualized histograms improved facial expression recognition performance when local features were employed. 展开更多
关键词 facial expression recognition local binary pattern weber local descriptor spatial context contextualized histogram
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Facial Expression Recognition Using Enhanced Convolution Neural Network with Attention Mechanism 认领 引用 被引量：7
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作者 K.Prabhu S.SathishKumar +2 位作者 M.Sivachitra S.Dineshkumar P.Sathiyabama 《Computer Systems Science & Engineering》 SCIE EI 2022年第4期415-426,共12页
Facial Expression Recognition(FER)has been an interesting area of research in places where there is human-computer interaction.Human psychol-ogy,emotions and behaviors can be analyzed in FER.Classifiers used in FER hav... Facial Expression Recognition(FER)has been an interesting area of research in places where there is human-computer interaction.Human psychol-ogy,emotions and behaviors can be analyzed in FER.Classifiers used in FER have been perfect on normal faces but have been found to be constrained in occluded faces.Recently,Deep Learning Techniques(DLT)have gained popular-ity in applications of real-world problems including recognition of human emo-tions.The human face reflects emotional states and human intentions.An expression is the most natural and powerful way of communicating non-verbally.Systems which form communications between the two are termed Human Machine Interaction(HMI)systems.FER can improve HMI systems as human expressions convey useful information to an observer.This paper proposes a FER scheme called EECNN(Enhanced Convolution Neural Network with Atten-tion mechanism)to recognize seven types of human emotions with satisfying results in its experiments.Proposed EECNN achieved 89.8%accuracy in classi-fying the images. 展开更多
关键词 Facial expression recognition linear discriminant analysis animal migration optimization regions of interest enhanced convolution neural network with attention mechanism
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Hybrid Convolutional Neural Network and Long Short-Term Memory Approach for Facial Expression Recognition 认领 引用 被引量：1
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作者 M.N.Kavitha A.RajivKannan 《Intelligent Automation & Soft Computing》 SCIE 2023年第1期689-704,共16页
Facial Expression Recognition(FER)has been an importantfield of research for several decades.Extraction of emotional characteristics is crucial to FERs,but is complex to process as they have significant intra-class va... Facial Expression Recognition(FER)has been an importantfield of research for several decades.Extraction of emotional characteristics is crucial to FERs,but is complex to process as they have significant intra-class variances.Facial characteristics have not been completely explored in static pictures.Previous studies used Convolution Neural Networks(CNNs)based on transfer learning and hyperparameter optimizations for static facial emotional recognitions.Particle Swarm Optimizations(PSOs)have also been used for tuning hyperparameters.However,these methods achieve about 92 percent in terms of accuracy.The existing algorithms have issues with FER accuracy and precision.Hence,the overall FER performance is degraded significantly.To address this issue,this work proposes a combination of CNNs and Long Short-Term Memories(LSTMs)called the HCNN-LSTMs(Hybrid CNNs and LSTMs)approach for FERs.The work is evaluated on the benchmark dataset,Facial Expression Recog Image Ver(FERC).Viola-Jones(VJ)algorithms recognize faces from preprocessed images followed by HCNN-LSTMs feature extractions and FER classifications.Further,the success rate of Deep Learning Techniques(DLTs)has increased with hyperparameter tunings like epochs,batch sizes,initial learning rates,regularization parameters,shuffling types,and momentum.This proposed work uses Improved Weight based Whale Optimization Algorithms(IWWOAs)to select near-optimal settings for these parameters using bestfitness values.The experi-mentalfindings demonstrated that the proposed HCNN-LSTMs system outper-forms the existing methods. 展开更多
关键词 Facial expression recognition Gaussianfilter hyperparameter optimization improved weight-based whale optimization algorithm deep learning(DL)
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Earthworm Optimization with Improved SqueezeNet Enabled Facial Expression Recognition Model 认领 引用
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作者 N.Sharmili Saud Yonbawi +5 位作者 Sultan Alahmari E.Laxmi Lydia Mohamad Khairi Ishak Hend Khalid Alkahtani Ayman Aljarbouh Samih M.Mostafa 《Computer Systems Science & Engineering》 SCIE EI 2023年第8期2247-2262,共16页
Facial expression recognition(FER)remains a hot research area among computer vision researchers and still becomes a challenge because of high intraclass variations.Conventional techniques for this problem depend on ha... Facial expression recognition(FER)remains a hot research area among computer vision researchers and still becomes a challenge because of high intraclass variations.Conventional techniques for this problem depend on hand-crafted features,namely,LBP,SIFT,and HOG,along with that a classifier trained on a database of videos or images.Many execute perform well on image datasets captured in a controlled condition;however not perform well in the more challenging dataset,which has partial faces and image variation.Recently,many studies presented an endwise structure for facial expression recognition by utilizing DL methods.Therefore,this study develops an earthworm optimization with an improved SqueezeNet-based FER(EWOISN-FER)model.The presented EWOISN-FER model primarily applies the contrast-limited adaptive histogram equalization(CLAHE)technique as a pre-processing step.In addition,the improved SqueezeNet model is exploited to derive an optimal set of feature vectors,and the hyperparameter tuning process is performed by the stochastic gradient boosting(SGB)model.Finally,EWO with sparse autoencoder(SAE)is employed for the FER process,and the EWO algorithm appropriately chooses the SAE parameters.Awide-ranging experimental analysis is carried out to examine the performance of the proposed model.The experimental outcomes indicate the supremacy of the presented EWOISN-FER technique. 展开更多
关键词 Facial expression recognition deep learning computer vision earthworm optimization hyperparameter optimization
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