Lin Wang¹, Juan Zhao², Hu Song³, Xiaolong Xu^4,*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1105-1135, 2024, DOI:10.32604/cmc.2024.048688

Abstract In smart classrooms, conducting multi-face expression recognition based on existing hardware devices to assess students’ group emotions can provide educators with more comprehensive and intuitive classroom effect analysis, thereby continuously promoting the improvement of teaching quality. However, most existing multi-face expression recognition 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 face images, which are of low quality and lack specificity, also restricting the development of this research. This paper aims to propose an end-to-end high-performance multi-face expression recognition algorithm model suitable… More >

Sardar Hasen Ali^*, Maiwan Bahjat Abdulrazzaq

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 429-448, 2024, DOI:10.32604/cmc.2024.048356

Abstract Handwritten character recognition (HCR) involves identifying characters in images, documents, and various sources such as forms surveys, questionnaires, and signatures, and transforming them into a machine-readable format for subsequent processing. Successfully recognizing complex and intricately shaped handwritten characters remains a significant obstacle. The use of convolutional neural network (CNN) in recent developments has notably advanced HCR, leveraging the ability to extract discriminative features from extensive sets of raw data. Because of the absence of pre-existing datasets in the Kurdish language, we created a Kurdish handwritten dataset called (KurdSet). The dataset consists of Kurdish characters, digits, texts, and symbols. The dataset… More >

Zhanfeng Wang¹, Lisha Yao^2,*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1659-1677, 2024, DOI:10.32604/cmc.2024.048304

Abstract 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 overcomes these limitations by vectorizing information through increased directionality and magnitude, ensuring that spatial information is not overlooked. Therefore, this study proposes a novel expression recognition technique called CAPSULE-VGG, which combines the strengths of CapsNet and convolutional neural networks. By refining and integrating features extracted by a convolutional neural network before introducing them into CapsNet, our model enhances facial recognition capabilities. Compared to traditional neural network models, our… More >

Zezheng Meng, Zefeng Cai, Jie Feng^*, Hanjie Ma, Haixiang Zhang, Shaohua Li

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1481-1496, 2024, DOI:10.32604/cmc.2024.048002

Abstract Optical braille recognition methods typically employ existing target detection models or segmentation models for the direct detection and recognition of braille characters in original braille images. However, these methods need improvement in accuracy and generalizability, especially in densely dotted braille image environments. This paper presents a two-stage braille recognition framework. The first stage is a braille dot detection algorithm based on Gaussian diffusion, targeting Gaussian heatmaps generated by the convex dots in braille images. This is applied to the detection of convex dots in double-sided braille, achieving high accuracy in determining the central coordinates of the braille convex dots. The… More >

Wuyan Liang¹, Xiaolong Xu^2,*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1089-1103, 2024, DOI:10.32604/cmc.2024.047861

Abstract Recognition of human gesture actions is a challenging issue due to the complex patterns in both visual and skeletal features. Existing gesture action recognition (GAR) methods typically analyze visual and skeletal data, failing to meet the demands of various scenarios. Furthermore, multi-modal approaches lack the versatility to efficiently process both uniform and disparate input patterns. Thus, in this paper, an attention-enhanced pseudo-3D residual model is proposed to address the GAR problem, called HgaNets. This model comprises two independent components designed for modeling visual RGB (red, green and blue) images and 3D skeletal heatmaps, respectively. More specifically, each component consists of… More >

Wei Wu^*, Yuan Zhang, Yunpeng Li, Chuanyang Li, Yan Hao

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 537-555, 2024, DOI:10.32604/cmes.2024.049174

Abstract Fusing hand-based features in multi-modal biometric recognition enhances anti-spoofing capabilities. Additionally, it leverages inter-modal correlation to enhance recognition performance. Concurrently, the robustness and recognition performance of the system can be enhanced through judiciously leveraging the correlation among multimodal features. Nevertheless, two issues persist in multi-modal feature fusion recognition: Firstly, the enhancement of recognition performance in fusion recognition has not comprehensively considered the inter-modality correlations among distinct modalities. Secondly, during modal fusion, improper weight selection diminishes the salience of crucial modal features, thereby diminishing the overall recognition performance. To address these two issues, we introduce an enhanced DenseNet multimodal recognition network… More > Graphic Abstract

Xianwei Jiang¹, Yanqiong Zhang^1,*, Juan Lei¹, Yudong Zhang^2,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 1-40, 2024, DOI:10.32604/cmes.2024.047649

Abstract Research on Chinese Sign Language (CSL) provides convenience and support for individuals with hearing impairments to communicate and integrate into society. This article reviews the relevant literature on Chinese Sign Language Recognition (CSLR) in the past 20 years. Hidden Markov Models (HMM), Support Vector Machines (SVM), and Dynamic Time Warping (DTW) were found to be the most commonly employed technologies among traditional identification methods. Benefiting from the rapid development of computer vision and artificial intelligence technology, Convolutional Neural Networks (CNN), 3D-CNN, YOLO, Capsule Network (CapsNet) and various deep neural networks have sprung up. Deep Neural Networks (DNNs) and their derived… More >

Xianghong Cao, Xinyu Wang, Xin Geng^*, Donghui Wu, Houru An

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 385-408, 2024, DOI:10.32604/cmes.2024.046752

Abstract This study proposes a pose estimation-convolutional neural network-bidirectional gated recurrent unit (PSE-CNN-BiGRU) fusion model for human posture recognition to address low accuracy issues in abnormal posture recognition due to the loss of some feature information and the deterioration of comprehensive performance in model detection in complex home environments. Firstly, the deep convolutional network is integrated with the Mediapipe framework to extract high-precision, multi-dimensional information from the key points of the human skeleton, thereby obtaining a human posture feature set. Thereafter, a double-layer BiGRU algorithm is utilized to extract multi-layer, bidirectional temporal features from the human posture feature set, and a… More >

Yanmei Shi¹, Wei Yu^2,*, Yanxia Zhao^3,*, Yungang Jia⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 887-906, 2024, DOI:10.32604/cmes.2024.046140

Abstract Web application fingerprint recognition is an effective security technology designed to identify and classify web applications, thereby enhancing the detection of potential threats and attacks. Traditional fingerprint recognition methods, which rely on preannotated feature matching, face inherent limitations due to the ever-evolving nature and diverse landscape of web applications. In response to these challenges, this work proposes an innovative web application fingerprint recognition method founded on clustering techniques. The method involves extensive data collection from the Tranco List, employing adjusted feature selection built upon Wappalyzer and noise reduction through truncated SVD dimensionality reduction. The core of the methodology lies in… More >

Zhijian Liu^*

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3787-3803, 2024, DOI:10.32604/cmc.2024.048998

Abstract Considering the variations in imaging sizes of the unmanned aerial vehicles (UAV) at different aerial photography heights, as well as the influence of factors such as light and weather, which can result in missed detection and false detection of the model, this paper presents a comprehensive detection model based on the improved lightweight You Only Look Once version 8s (YOLOv8s) algorithm used in natural light and infrared scenes (L_YOLO). The algorithm proposes a special feature pyramid network (SFPN) structure and substitutes most of the neck feature extraction module with the Special deformable convolution feature extraction module (SDCN). Moreover, the model… More >