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  • 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

    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, the overall FER performance is… 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

    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 from gamma axes and magnitudes… More >

  • Open Access

    ARTICLE

    Negative Emotions Sensitive Humanoid Robot with Attention-Enhanced Facial Expression Recognition Network

    Rongrong Ni1, Xiaofeng Liu1,*, Yizhou Chen1, Xu Zhou1, Huili Cai1, Loo Chu Kiong2

    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 149-164, 2022, DOI:10.32604/iasc.2022.026813

    Abstract Lonely older adults and persons restricted in movements are apt to cause negative emotions, which is harmful to their mental health. A humanoid robot with audiovisual interactions is presented, which can correspondingly output positive facial expressions to relieve human's negative facial expressions. The negative emotions are identified through an attention-enhanced facial expression recognition (FER) network. The network is firstly trained on MMEW macro-and micro-expression databases to discover expression-related features. Then, macro-expression recognition tasks are performed by fine-tuning the trained models on several benchmarking FER databases, including CK+ and Oulu-CASIA. A transformer network is introduced to process the sequential features engineered… More >

  • Open Access

    ARTICLE

    Facial Expression Recognition Based on the Fusion of Infrared and Visible Image

    Jiancheng Zou1, Jiaxin Li1,*, Juncun Wei1, Zhengzheng Li1, Xin Yang2

    Journal on Artificial Intelligence, Vol.3, No.3, pp. 123-134, 2021, DOI:10.32604/jai.2021.027069

    Abstract Facial expression recognition is a research hot spot in the fields of computer vision and pattern recognition. However, the existing facial expression recognition models are mainly concentrated in the visible light environment. They have insufficient generalization ability and low recognition accuracy, and are vulnerable to environmental changes such as illumination and distance. In order to solve these problems, we combine the advantages of the infrared and visible images captured simultaneously by array equipment our developed with two infrared and two visible lens, so that the fused image not only has the texture information of visible image, but also has the… More >

  • Open Access

    ARTICLE

    Game Outlier Behavior Detection System Based on Dynamic Time Warp Algorithm

    Shinjin Kang1, Soo Kyun Kim2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.1, pp. 219-237, 2022, DOI:10.32604/cmes.2022.018413

    Abstract This paper proposes a methodology for using multi-modal data in gameplay to detect outlier behavior. The proposed methodology collects, synchronizes, and quantifies time-series data from webcams, mouses, and keyboards. Facial expressions are varied on a one-dimensional pleasure axis, and changes in expression in the mouth and eye areas are detected separately. Furthermore, the keyboard and mouse input frequencies are tracked to determine the interaction intensity of users. Then, we apply a dynamic time warp algorithm to detect outlier behavior. The detected outlier behavior graph patterns were the play patterns that the game designer did not intend or play patterns that… More >

  • Open Access

    ARTICLE

    Automated Facial Expression Recognition and Age Estimation Using Deep Learning

    Syeda Amna Rizwan1, Yazeed Yasin Ghadi2, Ahmad Jalal1, Kibum Kim3,*

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5235-5252, 2022, DOI:10.32604/cmc.2022.023328

    Abstract With the advancement of computer vision techniques in surveillance systems, the need for more proficient, intelligent, and sustainable facial expressions and age recognition is necessary. The main purpose of this study is to develop accurate facial expressions and an age recognition system that is capable of error-free recognition of human expression and age in both indoor and outdoor environments. The proposed system first takes an input image pre-process it and then detects faces in the entire image. After that landmarks localization helps in the formation of synthetic face mask prediction. A novel set of features are extracted and passed to… More >

  • Open Access

    ARTICLE

    Face Recognition System Using Deep Belief Network and Particle Swarm Optimization

    K. Babu1,*, C. Kumar2, C. Kannaiyaraju3

    Intelligent Automation & Soft Computing, Vol.33, No.1, pp. 317-329, 2022, DOI:10.32604/iasc.2022.023756

    Abstract Facial expression for different emotional feelings makes it interesting for researchers to develop recognition techniques. Facial expression is the outcome of emotions they feel, behavioral acts, and the physiological condition of one’s mind. In the world of computer visions and algorithms, precise facial recognition is tough. In predicting the expression of a face, machine learning/artificial intelligence plays a significant role. The deep learning techniques are widely used in more challenging real-world problems which are highly encouraged in facial emotional analysis. In this article, we use three phases for facial expression recognition techniques. The principal component analysis-based dimensionality reduction techniques are… More >

  • Open Access

    ARTICLE

    Facial Expression Recognition Using Enhanced Convolution Neural Network with Attention Mechanism

    K. Prabhu1,*, S. SathishKumar2, M. Sivachitra3, S. Dineshkumar2, P. Sathiyabama4

    Computer Systems Science and Engineering, Vol.41, No.1, pp. 415-426, 2022, DOI:10.32604/csse.2022.019749

    Abstract Facial Expression Recognition (FER) has been an interesting area of research in places where there is human-computer interaction. Human psychology, 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 popularity in applications of real-world problems including recognition of human emotions. 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)… More >

  • Open Access

    ARTICLE

    An Efficient Adaptive Network-Based Fuzzy Inference System with Mosquito Host-Seeking For Facial Expression Recognition

    M. Carmel Sobia1, A. Abudhahir2

    Intelligent Automation & Soft Computing, Vol.24, No.4, pp. 869-881, 2018, DOI:10.31209/2018.100000014

    Abstract In this paper, an efficient facial expression recognition system using ANFIS-MHS (Adaptive Network-based Fuzzy Inference System with Mosquito Host-Seeking) has been proposed. The features were extracted using MLDA (Modified Linear Discriminant Analysis) and then the optimized parameters are computed by using mGSO (modified Glow-worm Swarm Optimization).The proposed system recognizes the facial expressions using ANFIS-MHS. The experimental results demonstrate that the proposed technique is performed better than existing classification schemes like HAKELM (Hybridization of Adaptive Kernel based Extreme Learning Machine), Support Vector Machine (SVM) and Principal Component Analysis (PCA). The proposed approach is implemented in MATLAB. More >

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