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  • Open Access


    Deep Facial Emotion Recognition Using Local Features Based on Facial Landmarks for Security System

    Youngeun An, Jimin Lee, EunSang Bak*, Sungbum Pan*

    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1817-1832, 2023, DOI:10.32604/cmc.2023.039460

    Abstract Emotion recognition based on facial expressions is one of the most critical elements of human-machine interfaces. Most conventional methods for emotion recognition using facial expressions use the entire facial image to extract features and then recognize specific emotions through a pre-trained model. In contrast, this paper proposes a novel feature vector extraction method using the Euclidean distance between the landmarks changing their positions according to facial expressions, especially around the eyes, eyebrows, nose, and mouth. Then, we apply a new classifier using an ensemble network to increase emotion recognition accuracy. The emotion recognition performance was compared with the conventional algorithms… More >

  • Open Access


    LF-CNN: Deep Learning-Guided Small Sample Target Detection for Remote Sensing Classification

    Chengfan Li1,2, Lan Liu3,*, Junjuan Zhao1, Xuefeng Liu4

    CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.1, pp. 429-444, 2022, DOI:10.32604/cmes.2022.019202

    Abstract Target detection of small samples with a complex background is always difficult in the classification of remote sensing images. We propose a new small sample target detection method combining local features and a convolutional neural network (LF-CNN) with the aim of detecting small numbers of unevenly distributed ground object targets in remote sensing images. The k-nearest neighbor method is used to construct the local neighborhood of each point and the local neighborhoods of the features are extracted one by one from the convolution layer. All the local features are aggregated by maximum pooling to obtain global feature representation. The classification… More >

  • Open Access


    Local Features-Based Watermarking for Image Security in Social Media

    Shady Y. El-mashad1, Amani M. Yassen1, Abdulwahab K. Alsammak1, Basem M. Elhalawany2,*

    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3857-3870, 2021, DOI:10.32604/cmc.2021.018660

    Abstract The last decade shows an explosion of using social media, which raises several challenges related to the security of personal files including images. These challenges include modifying, illegal copying, identity fraud, copyright protection and ownership of images. Traditional digital watermarking techniques embed digital information inside another digital information without affecting the visual quality for security purposes. In this paper, we propose a hybrid digital watermarking and image processing approach to improve the image security level. Specifically, variants of the widely used Least-Significant Bit (LSB) watermarking technique are merged with a blob detection algorithm to embed information into the boundary pixels… More >

  • Open Access


    Research on the Pedestrian Re-Identification Method Based on Local Features and Gait Energy Images

    Xinliang Tang1, Xing Sun1, Zhenzhou Wang1, Pingping Yu1, Ning Cao2, *, Yunfeng Xu3

    CMC-Computers, Materials & Continua, Vol.64, No.2, pp. 1185-1198, 2020, DOI:10.32604/cmc.2020.010283

    Abstract The appearance of pedestrians can vary greatly from image to image, and different pedestrians may look similar in a given image. Such similarities and variabilities in the appearance and clothing of individuals make the task of pedestrian re-identification very challenging. Here, a pedestrian re-identification method based on the fusion of local features and gait energy image (GEI) features is proposed. In this method, the human body is divided into four regions according to joint points. The color and texture of each region of the human body are extracted as local features, and GEI features of the pedestrian gait are also… More >

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