Face Recognition Smart Attendance System using Convolutional Neural Networks

被引:0
作者
Manimekalai, M. A. P. [1 ]
Daniel, Esther [1 ]
Neebha, T. Mary [1 ]
Muthulakshmi, K. [2 ]
Jess, C. Ryan Paul [1 ]
Raguram, S. [1 ]
机构
[1] Karunya Inst Technol & Sci, Coimbatore, India
[2] Sri Krishna Coll Engn & Technol, Coimbatore, India
来源
PRZEGLAD ELEKTROTECHNICZNY | 2024年 / 100卷 / 05期
关键词
Attendance; CNN (Convolutional Neural Networks); face images; extraction; IoT (Internet of Things);
D O I
10.15199/48.2024.05.46
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
. An automated face attendance system using Convolutional Neural Networks (CNN) is a promising technology for improving attendance management in educational institutions, workplaces, and other organizations. This system uses a deep learning model based on CNN to detect and recognize faces from images captured by a camera. The captured image is pre-processed by applying various techniques such as face detection, extraction, and normalization to extract facial features. The extracted features are then stored in a real-time database and used to train the CNN model to recognize the faces of individuals accurately. The system can efficiently handle various lighting conditions and pose variations to recognize individuals. The proposed method provides a fast and accurate approach to attendance management that can significantly reduce manual efforts and errors.
引用
收藏
页码:244 / 247
页数:4
相关论文
共 50 条
  • [31] Exploring Competitive Features Using Deep Convolutional Neural Network for Finger Vein Recognition
    Lu, Yu
    Xie, Shanjuan
    Wu, Shiqian
    IEEE ACCESS, 2019, 7 : 35113 - 35123
  • [32] On the estimation of face recognition system performance using image variability information
    Khan, Muhammad Aurangzeb
    Xydeas, Costas
    Ahmed, Hassan
    OPTIK, 2017, 136 : 619 - 632
  • [33] Using Deep Convolutional Neural Networks and Infrared Thermography to Identify Coal Quality and Gangue
    Eshaq, Refat Mohammed Abdullah
    Hu, Eryi
    Qaid, Hamzah A. A. M.
    Zhang, Yao
    Liu, Tonggang
    IEEE ACCESS, 2021, 9 : 147315 - 147327
  • [34] Automated vein verification using self-attention-based convolutional neural networks
    Kocakulak, Mustafa
    Avci, Adem
    Acir, Nurettin
    EXPERT SYSTEMS WITH APPLICATIONS, 2023, 230
  • [35] CONSTRUCTING AN EFFICIENT CLASSIFIER MODEL FOR NATURAL VEGETATION USING REGIONAL CONVOLUTIONAL NEURAL NETWORKS
    Vidhu, R.
    Niraimathi, S.
    INTERNATIONAL JOURNAL OF LIFE SCIENCE AND PHARMA RESEARCH, 2022, 12 : 115 - 127
  • [36] AUTOMATIC LABELING OF CORTICAL SULCI USING SPHERICAL CONVOLUTIONAL NEURAL NETWORKS IN A DEVELOPMENTAL COHORT
    Hao, Lingyan
    Bao, Shunxing
    Tang, Yucheng
    Gao, Riqiang
    Parvathaneni, Prasanna
    Miller, Jacob A.
    Voorhies, Willa
    Yao, Jewelia
    Bunge, Silvia A.
    Weiner, Kevin S.
    Landman, Bennett A.
    Lyu, Ilwoo
    2020 IEEE 17TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI 2020), 2020, : 412 - 415
  • [37] Joint Detection and Classification of Singing Voice Melody Using Convolutional Recurrent Neural Networks
    Kum, Sangeun
    Nam, Juhan
    APPLIED SCIENCES-BASEL, 2019, 9 (07):
  • [38] Vehicle license plate detection and recognition using deep neural networks and generative adversarial networks
    Zhang, Xiaoci
    Gu, Naijie
    Ye, Hong
    Lin, Chuanwen
    JOURNAL OF ELECTRONIC IMAGING, 2018, 27 (04)
  • [39] Composing Diverse Ensembles of Convolutional Neural Networks by Penalization
    Harangi, Balazs
    Baran, Agnes
    Beregi-Kovacs, Marcell
    Hajdu, Andras
    MATHEMATICS, 2023, 11 (23)
  • [40] Deprivation pockets through the lens of convolutional neural networks
    Wang, Jiong
    Kuffer, Monika
    Roy, Debraj
    Pfeffer, Karin
    REMOTE SENSING OF ENVIRONMENT, 2019, 234