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  • Open Access

    ARTICLE

    CapsNet-FR: Capsule Networks for Improved Recognition of Facial Features

    Mahmood Ul Haq1, Muhammad Athar Javed Sethi1, Najib Ben Aoun2,3, Ala Saleh Alluhaidan4,*, Sadique Ahmad5,6, Zahid farid7

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2169-2186, 2024, DOI:10.32604/cmc.2024.049645

    Abstract Face recognition (FR) technology has numerous applications in artificial intelligence including biometrics, security, authentication, law enforcement, and surveillance. Deep learning (DL) models, notably convolutional neural networks (CNNs), have shown promising results in the field of FR. However CNNs are easily fooled since they do not encode position and orientation correlations between features. Hinton et al. envisioned Capsule Networks as a more robust design capable of retaining pose information and spatial correlations to recognize objects more like the brain does. Lower-level capsules hold 8-dimensional vectors of attributes like position, hue, texture, and so on, which are… More >

  • Open Access

    ARTICLE

    Enhancing Security and Privacy in Distributed Face Recognition Systems through Blockchain and GAN Technologies

    Muhammad Ahmad Nawaz Ul Ghani1, Kun She1,*, Muhammad Arslan Rauf1, Shumaila Khan2, Javed Ali Khan3, Eman Abdullah Aldakheel4, Doaa Sami Khafaga4

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2609-2623, 2024, DOI:10.32604/cmc.2024.049611

    Abstract The use of privacy-enhanced facial recognition has increased in response to growing concerns about data security and privacy in the digital age. This trend is spurred by rising demand for face recognition technology in a variety of industries, including access control, law enforcement, surveillance, and internet communication. However, the growing usage of face recognition technology has created serious concerns about data monitoring and user privacy preferences, especially in context-aware systems. In response to these problems, this study provides a novel framework that integrates sophisticated approaches such as Generative Adversarial Networks (GANs), Blockchain, and distributed computing… More >

  • Open Access

    ARTICLE

    Optimizing Deep Neural Networks for Face Recognition to Increase Training Speed and Improve Model Accuracy

    Mostafa Diba*, Hossein Khosravi

    Intelligent Automation & Soft Computing, Vol.38, No.3, pp. 315-332, 2023, DOI:10.32604/iasc.2023.046590

    Abstract Convolutional neural networks continually evolve to enhance accuracy in addressing various problems, leading to an increase in computational cost and model size. This paper introduces a novel approach for pruning face recognition models based on convolutional neural networks. The proposed method identifies and removes inefficient filters based on the information volume in feature maps. In each layer, some feature maps lack useful information, and there exists a correlation between certain feature maps. Filters associated with these two types of feature maps impose additional computational costs on the model. By eliminating filters related to these categories… More >

  • Open Access

    ARTICLE

    Dataset of Large Gathering Images for Person Identification and Tracking

    Adnan Nadeem1,*, Amir Mehmood2, Kashif Rizwan3, Muhammad Ashraf4, Nauman Qadeer3, Ali Alzahrani1, Qammer H. Abbasi5, Fazal Noor1, Majed Alhaisoni6, Nadeem Mahmood7

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6065-6080, 2023, DOI:10.32604/cmc.2023.035012

    Abstract This paper presents a large gathering dataset of images extracted from publicly filmed videos by 24 cameras installed on the premises of Masjid Al-Nabvi, Madinah, Saudi Arabia. This dataset consists of raw and processed images reflecting a highly challenging and unconstraint environment. The methodology for building the dataset consists of four core phases; that include acquisition of videos, extraction of frames, localization of face regions, and cropping and resizing of detected face regions. The raw images in the dataset consist of a total of 4613 frames obtained from video sequences. The processed images in the… More >

  • Open Access

    REVIEW

    Broad Learning System for Tackling Emerging Challenges in Face Recognition

    Wenjun Zhang1, Wenfeng Wang2,3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.3, pp. 1597-1619, 2023, DOI:10.32604/cmes.2022.020517

    Abstract Face recognition has been rapidly developed and widely used. However, there is still considerable uncertainty in the computational intelligence based on human-centric visual understanding. Emerging challenges for face recognition are resulted from information loss. This study aims to tackle these challenges with a broad learning system (BLS). We integrated two models, IR3C with BLS and IR3C with a triplet loss, to control the learning process. In our experiments, we used different strategies to generate more challenging datasets and analyzed the competitiveness, sensitivity, and practicability of the proposed two models. In the model of IR3C with BLS, More >

  • Open Access

    ARTICLE

    Masked Face Recognition Using MobileNet V2 with Transfer Learning

    Ratnesh Kumar Shukla1,*, Arvind Kumar Tiwari2

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 293-309, 2023, DOI:10.32604/csse.2023.027986

    Abstract Corona virus (COVID-19) is once in a life time calamity that has resulted in thousands of deaths and security concerns. People are using face masks on a regular basis to protect themselves and to help reduce corona virus transmission. During the on-going coronavirus outbreak, one of the major priorities for researchers is to discover effective solution. As important parts of the face are obscured, face identification and verification becomes exceedingly difficult. The suggested method is a transfer learning using MobileNet V2 based technology that uses deep feature such as feature extraction and deep learning model,… More >

  • Open Access

    ARTICLE

    Suicide Ideation Detection of Covid Patients Using Machine Learning Algorithm

    R. Punithavathi1,*, S. Thenmozhi2, R. Jothilakshmi3, V. Ellappan4, Islam Md Tahzib Ul5

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 247-261, 2023, DOI:10.32604/csse.2023.025972

    Abstract During Covid pandemic, many individuals are suffering from suicidal ideation in the world. Social distancing and quarantining, affects the patient emotionally. Affective computing is the study of recognizing human feelings and emotions. This technology can be used effectively during pandemic for facial expression recognition which automatically extracts the features from the human face. Monitoring system plays a very important role to detect the patient condition and to recognize the patterns of expression from the safest distance. In this paper, a new method is proposed for emotion recognition and suicide ideation detection in COVID patients. This More >

  • Open Access

    ARTICLE

    Study on Real-Time Heart Rate Detection Based on Multi-People

    Qiuyu Hu1, Wu Zeng1,*, Yi Sheng1, Jian Xu1, Weihua Ou2, Ruochen Tan3

    Computer Systems Science and Engineering, Vol.44, No.2, pp. 1397-1408, 2023, DOI:10.32604/csse.2023.027980

    Abstract Heart rate is an important vital characteristic which indicates physical and mental health status. Typically heart rate measurement instruments require direct contact with the skin which is time-consuming and costly. Therefore, the study of non-contact heart rate measurement methods is of great importance. Based on the principles of photoelectric volumetric tracing, we use a computer device and camera to capture facial images, accurately detect face regions, and to detect multiple facial images using a multi-target tracking algorithm. Then after the regional segmentation of the facial image, the signal acquisition of the region of interest is More >

  • Open Access

    ARTICLE

    Voice to Face Recognition Using Spectral ERB-DMLP Algorithms

    Fauzi A. Bala1,2,*, Osman N. Ucan1, Oguz Bayat1

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 2187-2204, 2022, DOI:10.32604/cmc.2022.024205

    Abstract Designing an authentication system for securing the power plants are important to allow only specific staffs of the power plant to access the certain blocks so that they can be restricted from using high risk-oriented equipment. This authentication is also vital to prevent any security threats or risks like compromises of business server, release of confidential data etc. Though conventional works attempted to accomplish better authentication, they lacked with respect to accuracy. Hence, the study aims to enhance the recognition rate by introducing a voice recognition system as a personal authentication based on Deep Learning… More >

  • Open Access

    ARTICLE

    Hybrid Machine Learning Model for Face Recognition Using SVM

    Anil Kumar Yadav1, R. K. Pateriya2, Nirmal Kumar Gupta3, Punit Gupta4,*, Dinesh Kumar Saini4, Mohammad Alahmadi5

    CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2697-2712, 2022, DOI:10.32604/cmc.2022.023052

    Abstract Face recognition systems have enhanced human-computer interactions in the last ten years. However, the literature reveals that current techniques used for identifying or verifying faces are not immune to limitations. Principal Component Analysis-Support Vector Machine (PCA-SVM) and Principal Component Analysis-Artificial Neural Network (PCA-ANN) are among the relatively recent and powerful face analysis techniques. Compared to PCA-ANN, PCA-SVM has demonstrated generalization capabilities in many tasks, including the ability to recognize objects with small or large data samples. Apart from requiring a minimal number of parameters in face detection, PCA-SVM minimizes generalization errors and avoids overfitting problems More >

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