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Authentication with face recognition and sign language using ESP32-CAM

Year 2023, Volume: 25 Issue: 74, 481 - 489, 15.05.2023

Zafer Yalçın Oktay Türkdağlı Gökhan Dalkılıç Ömer Aydın

https://doi.org/10.21205/deufmd.2023257417

Abstract

Authentication is the confirmation of the accuracy of the data piece that any institution, person or system accepts as correct. Many methods are used for the authentication process. Some of these are the methods using biometric data such as face authentication, fingerprint authentication, and iris authentication. In this article, the way to create a secure system using face recognition and sign language as an authentication method is discussed and an application using ESP32-CAM is developed and tested. The results show that secure authentication cannot be achieved with facial recognition and sign language. The developed system is low cost and easy to implement. With this system, authentication can be done without requiring any physical contact, and it can be used for personal security and entrance and exit. Sign language, which is frequently used by hearing-impaired individuals, can play an active role as authentication. With low-cost modules such as ESP32, it will be an alternative to authentication in almost every environment.

Keywords

Authentication Face recognition Sign language ESP32-Cam

References

  • [1] Evwiekpaefe, A.E., Eyinla, V.O. 2021. Implementing fingerprint authentication in computer-based tests. Nigerian Journal of Technology, Vol. 40(2), pp. 284-291.
  • [2] Fong, S., Zhuang, Y., Fister, I., Fister, Jr. I. 2013. A biometric authentication model using hand gesture images. BioMed Engineering OnLine, Vol. 12(111), pp. 1-18.
  • [3] Ibrahim, D.R., The, J.S., Abdullah, R. 2021. Multifactor authentication system based on color visual cryptography, facial recognition, and dragonfly optimization. Information Security Journal: A Global Perspective, Vol. 30(3), pp. 149-159.
  • [4] Lin, W.H., Wu, B.H., Huang, Q.H. 2018. A face-recognition approach based on secret sharing for user authentication in public-transportation security. 2018 IEEE International Conference on Applied System Invention (ICASI), Chiba &Tokyo, Japan, 13-17 April 2018.
  • [5] Gayathri, M., Malathy, C. 2021. Novel framework for multimodal biometric image authentication using visual share neural network. Pattern Recognition Letters, Vol. 152(December 2021), pp. 1-9.
  • [6] Badgujar, M., Wagh, A., Chavan, S., Chumbhale, P., Sonawane, R.C. 2022. IoT Based Automatic Door Lock System by Face and Voice Recognition. International Research Journal of Modernization in Engineering Technology and Science, Vol. 4(3), pp. 542-545.
  • [7] Van Murugiah, K., Subhashini, G., Abdulla, R. 2021. Wearable IOT based Malaysian sign language recognition and text translation system. Journal of Applied Technology and Innovation, Vol. 5(4), pp. 51-58.
  • [8] Ajay, S., Potluri, A., George, S.M., Gaurav, R., Anusri, S., 2021. Indian Sign Language Recognition Using Random Forest Classifier. 2021 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), Bangalore, India, 9-11 July 2021.
  • [9] Palarimath, S., Blessing, N. R., Sujatha, T., Pyingkodi, M., Ugalde, B. H., & Palarimath, R. D. 2022. A Robust Authentication and Authorization System Powered by Deep Learning and Incorporating Hand Signals. In Intelligent Data Communication Technologies and Internet of Things (pp. 1061-1071). Springer, Singapore.
  • [10] Rwelli, R. E., Shahin, O. R., & Taloba, A. I. 2022. Gesture based Arabic Sign Language Recognition for Impaired People based on Convolution Neural Network. arXiv preprint arXiv:2203.05602.
  • [11] Harini, N., Lavanya, P., & Sravya, G. V. N. S. K. 2022. High-Security Locking System Using Arduino.
  • [12] Reddy, K. Y., Reddy, A. J., Reddy, K. B. P., & Rao, M. B. S. 2022. IoT Based Smart Door Lock System.

ESP32-CAM kullanarak yüz tanıma ve işaret dili ile kimlik doğrulama

Year 2023, Volume: 25 Issue: 74, 481 - 489, 15.05.2023

Zafer Yalçın Oktay Türkdağlı Gökhan Dalkılıç Ömer Aydın

https://doi.org/10.21205/deufmd.2023257417

Abstract

ESP32-CAM Kullanılarak Yüz Tanıma ve İşaret Dili ile Kimlik Doğrulama Kimlik doğrulama, herhangi bir kurum, kişi veya sistemin doğru kabul ettiği veri parçasının doğruluğunun teyididir. Kimlik doğrulama işlemi için birçok yöntem kullanılmaktadır. Bunlardan bazıları yüzle kimlik doğrulama, parmak iziyle kimlik doğrulama ve iris kimlik doğrulama gibi biyometrik verileri kullanan yöntemlerdir. Bu makalede, kimlik doğrulama yöntemi olarak yüz tanıma ve işaret dilini kullanarak güvenli bir sistem oluşturmanın yolu tartışılmakta ve ESP32-CAM kullanan bir uygulama geliştirilip test edilmektedir. Sonuçlar, yüz tanıma ve işaret dili ile güvenli kimlik doğrulamanın sağlanamayacağını göstermektedir. Geliştirilen sistem düşük maliyetli ve uygulaması kolaydır. Bu sistem ile herhangi bir fiziksel temas gerektirmeden kimlik doğrulama yapılabilmekte, kişisel güvenlik ve giriş çıkış için kullanılabilmektedir. İşitme engelli bireylerin sıklıkla kullandığı işaret dili, kimlik doğrulama olarak aktif rol oynayabilir. ESP32 gibi düşük maliyetli modüller ile neredeyse her ortamda kimlik doğrulamaya alternatif olacaktır.

Keywords

Kimlik doğrulama Yüz tanıma İşaret dili ESP32-Cam

References

  • [1] Evwiekpaefe, A.E., Eyinla, V.O. 2021. Implementing fingerprint authentication in computer-based tests. Nigerian Journal of Technology, Vol. 40(2), pp. 284-291.
  • [2] Fong, S., Zhuang, Y., Fister, I., Fister, Jr. I. 2013. A biometric authentication model using hand gesture images. BioMed Engineering OnLine, Vol. 12(111), pp. 1-18.
  • [3] Ibrahim, D.R., The, J.S., Abdullah, R. 2021. Multifactor authentication system based on color visual cryptography, facial recognition, and dragonfly optimization. Information Security Journal: A Global Perspective, Vol. 30(3), pp. 149-159.
  • [4] Lin, W.H., Wu, B.H., Huang, Q.H. 2018. A face-recognition approach based on secret sharing for user authentication in public-transportation security. 2018 IEEE International Conference on Applied System Invention (ICASI), Chiba &Tokyo, Japan, 13-17 April 2018.
  • [5] Gayathri, M., Malathy, C. 2021. Novel framework for multimodal biometric image authentication using visual share neural network. Pattern Recognition Letters, Vol. 152(December 2021), pp. 1-9.
  • [6] Badgujar, M., Wagh, A., Chavan, S., Chumbhale, P., Sonawane, R.C. 2022. IoT Based Automatic Door Lock System by Face and Voice Recognition. International Research Journal of Modernization in Engineering Technology and Science, Vol. 4(3), pp. 542-545.
  • [7] Van Murugiah, K., Subhashini, G., Abdulla, R. 2021. Wearable IOT based Malaysian sign language recognition and text translation system. Journal of Applied Technology and Innovation, Vol. 5(4), pp. 51-58.
  • [8] Ajay, S., Potluri, A., George, S.M., Gaurav, R., Anusri, S., 2021. Indian Sign Language Recognition Using Random Forest Classifier. 2021 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), Bangalore, India, 9-11 July 2021.
  • [9] Palarimath, S., Blessing, N. R., Sujatha, T., Pyingkodi, M., Ugalde, B. H., & Palarimath, R. D. 2022. A Robust Authentication and Authorization System Powered by Deep Learning and Incorporating Hand Signals. In Intelligent Data Communication Technologies and Internet of Things (pp. 1061-1071). Springer, Singapore.
  • [10] Rwelli, R. E., Shahin, O. R., & Taloba, A. I. 2022. Gesture based Arabic Sign Language Recognition for Impaired People based on Convolution Neural Network. arXiv preprint arXiv:2203.05602.
  • [11] Harini, N., Lavanya, P., & Sravya, G. V. N. S. K. 2022. High-Security Locking System Using Arduino.
  • [12] Reddy, K. Y., Reddy, A. J., Reddy, K. B. P., & Rao, M. B. S. 2022. IoT Based Smart Door Lock System.
There are 12 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Zafer Yalçın 0000-0003-0207-6693

Oktay Türkdağlı 0000-0002-4487-7627

Gökhan Dalkılıç 0000-0002-0130-1716

Ömer Aydın 0000-0002-7137-4881

Early Pub Date May 12, 2023
Publication Date May 15, 2023
Published in Issue Year 2023 Volume: 25 Issue: 74

Cite

APA Yalçın, Z., Türkdağlı, O., Dalkılıç, G., Aydın, Ö. (2023). Authentication with face recognition and sign language using ESP32-CAM. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen Ve Mühendislik Dergisi, 25(74), 481-489. https://doi.org/10.21205/deufmd.2023257417
AMA Yalçın Z, Türkdağlı O, Dalkılıç G, Aydın Ö. Authentication with face recognition and sign language using ESP32-CAM. DEUFMD. May 2023;25(74):481-489. doi:10.21205/deufmd.2023257417
Chicago Yalçın, Zafer, Oktay Türkdağlı, Gökhan Dalkılıç, and Ömer Aydın. “Authentication With Face Recognition and Sign Language Using ESP32-CAM”. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen Ve Mühendislik Dergisi 25, no. 74 (May 2023): 481-89. https://doi.org/10.21205/deufmd.2023257417.
EndNote Yalçın Z, Türkdağlı O, Dalkılıç G, Aydın Ö (May 1, 2023) Authentication with face recognition and sign language using ESP32-CAM. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi 25 74 481–489.
IEEE Z. Yalçın, O. Türkdağlı, G. Dalkılıç, and Ö. Aydın, “Authentication with face recognition and sign language using ESP32-CAM”, DEUFMD, vol. 25, no. 74, pp. 481–489, 2023, doi: 10.21205/deufmd.2023257417.
ISNAD Yalçın, Zafer et al. “Authentication With Face Recognition and Sign Language Using ESP32-CAM”. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi 25/74 (May 2023), 481-489. https://doi.org/10.21205/deufmd.2023257417.
JAMA Yalçın Z, Türkdağlı O, Dalkılıç G, Aydın Ö. Authentication with face recognition and sign language using ESP32-CAM. DEUFMD. 2023;25:481–489.
MLA Yalçın, Zafer et al. “Authentication With Face Recognition and Sign Language Using ESP32-CAM”. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen Ve Mühendislik Dergisi, vol. 25, no. 74, 2023, pp. 481-9, doi:10.21205/deufmd.2023257417.
Vancouver Yalçın Z, Türkdağlı O, Dalkılıç G, Aydın Ö. Authentication with face recognition and sign language using ESP32-CAM. DEUFMD. 2023;25(74):481-9.

Dokuz Eylül Üniversitesi, Mühendislik Fakültesi Dekanlığı Tınaztepe Yerleşkesi, Adatepe Mah. Doğuş Cad. No: 207-I / 35390 Buca-İZMİR.