Autors: Stoimenov, S., Tsenov, G. T., Mladenov, V. M.
Title: Face recognition system in Android using neural networks
Keywords: face recognition, Feed-forward Neural Network, mobile applic

Abstract: This paper presents a face recognition system developed in Android for robust mobile phone face unlock verification of several users. For the classification task standard neural networks structures in Java are used as a classifier by implementing the classical Feed-forward Neural Network (NN) with back propagation algorithm.

References

    Issue

    2016 13th Symposium on Neural Networks and Applications, NEUREL 2016, pp. 125-128, 2016, Serbia, Institute of Electrical and Electronics Engineers Inc., DOI 10.1109/NEUREL.2016.7800138

    Цитирания (Citation/s):
    1. Zhang, X., He, T., Xu, X. Android-based smartphone authentication system using biometric techniques: A review, Proceedings - 2019 4th International Conference on Control, Robotics and Cybernetics, CRC 2019 9058852, pp. 104-108 - 2019 - в издания, индексирани в Scopus или Web of Science
    2. Raghul, S., Mohankumar, N. Microcontroller Based ANN for Pick and Place Robot Coordinate Monitoring System., Lecture Notes in Electrical Engineering, 601, Volume 601, 2020, pp. 340-3481st International Conference on Data Science, Machine Learning and Applications, 2019; Hyderabad; India; 29, Springer, DOI: 10.1007/978-981-15-1420-3_35, pp. 340-348 - 2020 - в издания, индексирани в Scopus или Web of Science
    3. 35. Martinez-Alpiste, I., Golcarenarenji, G., Wang, Q. and Alcaraz-Calero, J.M., 2021. Smartphone-based real-time object recognition architecture for portable and constrained systems. Journal of Real-Time Image Processing, pp.1-13. - 2021 - в издания, индексирани в Scopus или Web of Science
    4. 42) Jose, C.J. and Rajasree, M.S., 2022. Deep Learning-Based Implicit Continuous Authentication of Smartphone User. In Proceedings of Third International Conference on Communication, Computing and Electronics Systems (pp. 387-400). Springer, Singapore. doi.org/10.1007/978-981-16-8862-1_25 (Google Scholar, Scopus) - 2022 - в издания, индексирани в Scopus или Web of Science
    5. Pryshchenko, O.A., Plakhtii, V., Dumin, O.M., Pochanin, G.P., Ruban, V.P., Capineri, L. and Crawford, F., 2022. “Implementation of an Artificial Intelligence Approach to GPR Systems for Landmine Detection,” Remote Sensing, vol. 14, issue (17), p. 4421., https://doi.org/10.3390/rs14174421 (Web of Science, Google Scholar) IF 5.786, SJR 1.283 - 2022 - в издания, индексирани в Scopus или Web of Science
    6. Bisen, D., Shukla, R., Rajpoot, N., Maurya, P. and Uttam, A.K., 2022. Responsive human-computer interaction model based on recognition of facial landmarks using machine learning algorithms. Multimedia Tools and Applications, vol. 81, issue (13), pp. 18011-18031., ISSN 13807501, DOI 10.1007/s11042-022-12775-6 (Web of Science, Scopus) IF 2.396, SJR 0.716 - 2022 - в издания, индексирани в Scopus или Web of Science
    7. Wangean, D.A., Setyawan, S., Maulana, F.I., Pangestu, G. and Huda, C., 2023, February. Development of Real-Time Face Recognition for Smart Door Lock Security System using Haar Cascade and OpenCV LBPH Face Recognizer. In 2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE) (pp. 506-510). IEEE. ISBN 979-835032095-4, DOI 10.1109/ICCoSITE57641.2023.10127753 (Scopus, Google Scholar) - 2023 - в издания, индексирани в Scopus или Web of Science
    8. Kolekar, S., Patil, P., Barge, P. and Kosare, T., 2023, June. A Comprehensive Study on Artificial Intelligence-Based Face Recognition Technologies. In International Conference on IoT Based Control Networks and Intelligent Systems (pp. 249-263). Singapore: Springer Nature Singapore. ISSN 23673370, ISBN 978-981996585-4, DOI 10.1007/978-981-99-6586-1_17 (Scopus, Google Scholar) SJR 0.151 - 2023 - в издания, индексирани в Scopus или Web of Science

    Вид: публикация в международен форум, публикация в реферирано издание, индексирана в Scopus и Web of Science