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  • Open Access

    ARTICLE

    A Multi-Modal Deep Learning Approach for Emotion Recognition

    H. M. Shahzad1,3, Sohail Masood Bhatti1,3,*, Arfan Jaffar1,3, Muhammad Rashid2

    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1561-1570, 2023, DOI:10.32604/iasc.2023.032525 - 05 January 2023

    Abstract In recent years, research on facial expression recognition (FER) under mask is trending. Wearing a mask for protection from Covid 19 has become a compulsion and it hides the facial expressions that is why FER under the mask is a difficult task. The prevailing unimodal techniques for facial recognition are not up to the mark in terms of good results for the masked face, however, a multimodal technique can be employed to generate better results. We proposed a multimodal methodology based on deep learning for facial recognition under a masked face using facial and vocal… More >

  • Open Access

    ARTICLE

    A Novel Efficient Patient Monitoring FER System Using Optimal DL-Features

    Mousa Alhajlah*

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6161-6175, 2023, DOI:10.32604/cmc.2023.032505 - 28 December 2022

    Abstract Automated Facial Expression Recognition (FER) serves as the backbone of patient monitoring systems, security, and surveillance systems. Real-time FER is a challenging task, due to the uncontrolled nature of the environment and poor quality of input frames. In this paper, a novel FER framework has been proposed for patient monitoring. Preprocessing is performed using contrast-limited adaptive enhancement and the dataset is balanced using augmentation. Two lightweight efficient Convolution Neural Network (CNN) models MobileNetV2 and Neural search Architecture Network Mobile (NasNetMobile) are trained, and feature vectors are extracted. The Whale Optimization Algorithm (WOA) is utilized to More >

  • Open Access

    ARTICLE

    An Analysis Model of Learners’ Online Learning Status Based on Deep Neural Network and Multi-Dimensional Information Fusion

    Mingyong Li1, Lirong Tang1, Longfei Ma1, Honggang Zhao1, Jinyu Hu1, Yan Wei1,2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.3, pp. 2349-2371, 2023, DOI:10.32604/cmes.2023.022604 - 23 November 2022

    Abstract The learning status of learners directly affects the quality of learning. Compared with offline teachers, it is difficult for online teachers to capture the learning status of students in the whole class, and it is even more difficult to continue to pay attention to students while teaching. Therefore, this paper proposes an online learning state analysis model based on a convolutional neural network and multi-dimensional information fusion. Specifically, a facial expression recognition model and an eye state recognition model are constructed to detect students’ emotions and fatigue, respectively. By integrating the detected data with the More >

  • Open Access

    ARTICLE

    Human-Computer Interaction Using Deep Fusion Model-Based Facial Expression Recognition System

    Saiyed Umer1,*, Ranjeet Kumar Rout2, Shailendra Tiwari3, Ahmad Ali AlZubi4, Jazem Mutared Alanazi4, Kulakov Yurii5

    CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.2, pp. 1165-1185, 2023, DOI:10.32604/cmes.2022.023312 - 27 October 2022

    Abstract A deep fusion model is proposed for facial expression-based human-computer Interaction system. Initially, image preprocessing, i.e., the extraction of the facial region from the input image is utilized. Thereafter, the extraction of more discriminative and distinctive deep learning features is achieved using extracted facial regions. To prevent overfitting, in-depth features of facial images are extracted and assigned to the proposed convolutional neural network (CNN) models. Various CNN models are then trained. Finally, the performance of each CNN model is fused to obtain the final decision for the seven basic classes of facial expressions, i.e., fear, More >

  • Open Access

    ARTICLE

    Facial Expression Recognition Based on Multi-Channel Attention Residual Network

    Tongping Shen1,2,*, Huanqing Xu1

    CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.1, pp. 539-560, 2023, DOI:10.32604/cmes.2022.022312 - 29 September 2022

    Abstract 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 More >

  • Open Access

    ARTICLE

    Data Mining Approach Based on Hierarchical Gaussian Mixture Representation Model

    Hanan A. Hosni Mahmoud1,*, Alaaeldin M. Hafez2, Fahd Althukair3

    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3727-3741, 2023, DOI:10.32604/iasc.2023.031442 - 17 August 2022

    Abstract Infinite Gaussian mixture process is a model that computes the Gaussian mixture parameters with order. This process is a probability density distribution with adequate training data that can converge to the input density curve. In this paper, we propose a data mining model namely Beta hierarchical distribution that can solve axial data modeling. A novel hierarchical Two-Hyper-Parameter Poisson stochastic process is developed to solve grouped data modelling. The solution uses data mining techniques to link datum in groups by linking their components. The learning techniques are novel presentations of Gaussian modelling that use prior knowledge More >

  • Open Access

    ARTICLE

    Emotion Exploration in Autistic Children as an Early Biomarker through R-CNN

    S. P. Abirami1,*, G. Kousalya1, R. Karthick2

    Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 595-607, 2023, DOI:10.32604/iasc.2023.027562 - 06 June 2022

    Abstract Autism Spectrum Disorder (ASD) is found to be a major concern among various occupational therapists. The foremost challenge of this neurodevelopmental disorder lies in the fact of analyzing and exploring various symptoms of the children at their early stage of development. Such early identification could prop up the therapists and clinicians to provide proper assistive support to make the children lead an independent life. Facial expressions and emotions perceived by the children could contribute to such early intervention of autism. In this regard, the paper implements in identifying basic facial expression and exploring their emotions… More >

  • Open Access

    ARTICLE

    Hybrid Convolutional Neural Network and Long Short-Term Memory Approach for Facial Expression Recognition

    M. N. Kavitha1,*, A. RajivKannan2

    Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 689-704, 2023, DOI:10.32604/iasc.2023.025437 - 06 June 2022

    Abstract Facial Expression Recognition (FER) has been an important field 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,… More >

  • Open Access

    ARTICLE

    4D Facial Expression Recognition Using Geometric Landmark-based Axes-angle Feature Extraction

    Henry Ugochukwu Ukwu*, Kamil Yurtkan

    Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 1819-1838, 2022, DOI:10.32604/iasc.2022.025695 - 25 May 2022

    Abstract The primary goal of this paper is to describe a proposed framework for identifying human face expressions. A methodology has been proposed and developed to identify facial emotions using an axes-angular feature extracted from facial landmarks for 4D dynamic facial expression video data. The 4D facial expression recognition (FER) problem is modeled as an unbalanced problem using the full video sequence. The proposed dataset includes landmarks that are positioned to be fiducial features: around the brows, eyes, nose, cheeks, and lips. Following the initial facial landmark preprocessing, feature extraction is carried out. Input feature vectors… More >

  • Open Access

    ARTICLE

    Research on Facial Expression Capture Based on Two-Stage Neural Network

    Zhenzhou Wang1, Shao Cui1, Xiang Wang1,*, JiaFeng Tian2

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4709-4725, 2022, DOI:10.32604/cmc.2022.027767 - 21 April 2022

    Abstract To generate realistic three-dimensional animation of virtual character, capturing real facial expression is the primary task. Due to diverse facial expressions and complex background, facial landmarks recognized by existing strategies have the problem of deviations and low accuracy. Therefore, a method for facial expression capture based on two-stage neural network is proposed in this paper which takes advantage of improved multi-task cascaded convolutional networks (MTCNN) and high-resolution network. Firstly, the convolution operation of traditional MTCNN is improved. The face information in the input image is quickly filtered by feature fusion in the first stage and… More >

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