KaydedilenlerDerginizi ekleyin

Dergiler / International Journal of Intelligent Systems and Applications in Engineering / 2022 / Cilt: 10 - Sayı: 1

Face and Hand Gesture Recognition Based Person Identification System using Convolutional Neural Network

Shahidul Islam KHAN
Mysha Sarin KABİSHA
Kazi Anisa RAHİM
Md. KHALİLUZZAMAN
Sayfa
105–115
Yayın Tarihi
2022-10-18
DOI
—
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

With two customized dataset our face model gained 98.00% accuracy and hand gesture (number sign) model gained an accuracy of 98.33%.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:Person identification system is now become the most hyped system for security purpose.
📊
Temel Bulgular & Veri:This proposed system works on two models, one is a VGG16 architecture model for face recognition and another model is for hand gesture which is based on simple CNN with two convolutional layers.
💡
Sonuç & Katkı:With two customized dataset our face model gained 98.00% accuracy and hand gesture (number sign) model gained an accuracy of 98.33%.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Alan Parametreleri

Abstract

Person identification system is now become the most hyped system for security purpose. It’s also gaining a lot of attention in the field of computer vision. For verification of human, facial recognition and hand gesture recognition are the most common topics of research. In the current days, various researchers focused on facial and hand gesture recognition using various shallow techniques and Deep Convolutional Neural Network (DCNN). However, using one feature of human for person identification is the most researched topic till now. In this paper, we proposed a Convolutional Neural Network (CNN) based system which will identify a person using two traits i.e., face and hand gesture of number sign of that person. For feature extraction and recognition Neural Network have shown immense good result. This proposed system works on two models, one is a VGG16 architecture model for face recognition and another model is for hand gesture which is based on simple CNN with two convolutional layers. With two customized dataset our face model gained 98.00% accuracy and hand gesture (number sign) model gained an accuracy of 98.33%.

🔖

Bu Makaleye Atıf Yap

KHAN, S. I., KABİSHA, M. S., RAHİM, K. A., KHALİLUZZAMAN, M. (2022). Face and Hand Gesture Recognition Based Person Identification System using Convolutional Neural Network. International Journal of Intelligent Systems and Applications in Engineering, Cilt: 10 - Sayı: 1, 105–115. https://ajindex.com/articles/541263

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (11)

  1. B. Kranthikiran, and P. Pulicherla, “Face Detection and Recognition for use in Campus Surveillance,” International Journal of Innovative Technology and Exploring Engineering, vol. 9, no. 3, pp. 2908–2913, Jan. 2020, doi: 10.35940/ijitee.b7682.019320
  2. M. Coşkun, A. Uçar, Ö. Yildirim, and Y. Demir, “A FACE RECOGNITION ALGORITHM BASED ON CONVOLUTIONAL NEURAL NETWORK,” REVISTA ARGENTINA DE CLINICA PSICOLOGICA, 2020, doi: 10.24205/03276716.2020.339
  3. Y. Said, M. Barr, and H. E. Ahmed, “Design of a Face Recognition System based on Convolutional Neural Network (CNN),” Engineering, Technology & Applied Science Research, vol. 10, no. 3, pp. 5608–5612, Jun. 2020, doi: 10.48084/etasr.3490
  4. K. B. Pranav, and J. Manikandan, “Design and Evaluation of a Real- Time Face Recognition System using Convolutional Neural Networks,” Procedia Computer Science, vol. 171, pp. 1651–1659, Jan. 2020, doi: 10.1016/j.procs.2020.04.177
  5. G. Hu, Y. Yang, D. Yi, J. Kittler, W. Christmas, S. Z. Li, & T. Hospedales, “When face recognition meets with deep learning: an evaluation of convolutional neural networks for face recognition,” In Proceedings of the IEEE international conference on computer vision workshops, pp. 142-150, IEEE, 2015
  6. Z. Pei, H. Xu, Y. Zhang, M. Guo, and Y.-H. Yang, “Face Recognition via Deep Learning Using Data Augmentation Based on Orthogonal Experiments,” Electronics, vol. 8, no. 10, p. 1088, Sep. 2019, doi: 10.3390/electronics8101088
  7. P. Gupta, N. Saxena, M. Sharma, and J. Tripathi, “Deep learning model for group face recognition based on Convolution Neural Network,” Journal of Xidian University, vol. 14, no. 5, May 2020, doi: 10.37896/jxu14.5/415
  8. Z. Chen, J.-T. Kim, J. Liang, J. Zhang, and Y.-B. Yuan, “Real- Time Hand Gesture Recognition Using Finger Segmentation,” The Scientific World Journal, vol. 2014, p. e267872, Jun. 2014, doi: 10.1155/2014/267872
  9. R. F. Pinto, C. D. B. Borges, A. M. A. Almeida, and I. C. Paula, “Static Hand Gesture Recognition Based on Convolutional Neural Networks,” Journal of Electrical and Computer Engineering, vol. 2019, pp. 1–12, Oct. 2019, doi: 10.1155/2019/4167890
  10. J. Chen, S. Bi, G. Zhang, and G. Cao, “High-Density Surface EMG-Based Gesture Recognition Using a 3D Convolutional Neural Network,” Sensors, vol. 20, no. 4, p. 1201, Feb. 2020, doi: 10.3390/s20041201
  11. P. Molchanov, S. Gupta, K. Kim, & J. Kautz, “Hand gesture recognition with 3D convolutional neural network,” In Proceedings of the IE