nach oben

Erschienen in:

2022 | OriginalPaper | Buchkapitel

A Novel Approach for Detecting Facial Key Points Using Convolution Neural Networks

verfasst von : Rishi Kakkar, Y. V. Srinivasa Murthy

Erschienen in: Artificial Intelligence and Speech Technology

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

The task of face recognition is having many real-time applications in which the process of facial keypoint detection is considered to be an intermediate and crucial step. The amount of keypoints that are using for face recognition decides the computational requirements of the algorithm. In this paper, an effort has been made to detect the useful 15 facial key points using convolutional neural networks and compared with the state-of-the-art system with 30 facial key points. We made an effort to identify the 15 facial key points (6 points from eye +4 points from eyebrows +4 points from lips +1 point from the nose) by using the proper hyperparameters for convolutional neural network. It is found that the performance of the proposed system is quite similar when compared to the system with 30 facial key points.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel A Novel Approach to Detect Face Mask in Real Time

Nächstes Kapitel Effective Hyperspectral Image Classification Using Learning Models

https://www.kaggle.com/c/facial-keypoints-detection.

Buck, S.F.: A method of estimation of missing values in multivariate data suitable for use with an electronic computer. J. R. Stat. Soc. Ser. (Methodological) 22(2), 302–306 (1960)MathSciNetMATH

Buitinck, L., et al.: API design for machine learning software: experiences from the scikit-learn project. arXiv preprint arXiv:1309.0238 (2013)

Mehdi Cherrat, E., Alaoui, R., Bouzahir, H.: Convolutional neural networks approach for multimodal biometric identification system using the fusion of fingerprint, finger-vein and face images. Peer J. Comput. Sci. 6, e248 (2020)

Dantcheva, A., Elia, P., Ross, A.: What else does your biometric data reveal? a survey on soft biometrics. IEEE Trans. Inf. Forensics Secur. 11(3), 441–467 (2015)CrossRef

Gupta, K.D., Ahsan, M., Andrei, S., Alam, K.M.R.: A robust approach of facial orientation recognition from facial features. BRAIN. Broad Res. Artif. Intell. Neurosci. 8(3), 5–12 (2017)

Karczmarek, P., Pedrycz, W., Kiersztyn, A., Rutka, P.: A study in facial features saliency in face recognition: an analytic hierarchy process approach. Soft Comput. 21(24), 7503–7517 (2016). https://doi.org/10.1007/s00500-016-2305-9CrossRef

Khan, S., Javed, M.H., Ahmed, E., Shah, S.A., Ali, S.U.: Facial recognition using convolutional neural networks and implementation on smart glasses. In: 2019 International Conference on Information Science and Communication Technology (ICISCT), pp. 1–6. IEEE (2019)

Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)

Latha, P., Ganesan, L., Annadurai, S.: Face recognition using neural networks. Signal Process. Int. J. (SPIJ) 3(5), 153–160 (2009)

10.

Masters, D., Luschi, C.: Revisiting small batch training for deep neural networks. arXiv preprint arXiv:1804.07612 (2018)

11.

Min, W., Fan, M., Li, J., Han, Q.: Real-time face recognition based on pre-identification and multi-scale classification. IET Comput. Vis. 13(2), 165–171 (2019)CrossRef

12.

Owayjan, M., Dergham, A., Haber, G., Fakih, N., Hamoush, A., Abdo, E.: Face recognition security system. In: Elleithy, K., Sobh, T. (eds.) New Trends in Networking, Computing, E-learning, Systems Sciences, and Engineering, pp. 343–348. Springer (2015). https://doi.org/10.1007/978-3-319-06764-3_42

13.

Radiuk, P.M.: Impact of training set batch size on the performance of convolutional neural networks for diverse datasets. Inf. Technol. Manag. Sci. 20(1), 20–24 (2017)

14.

Rajni, D.K.: An efficient method of PCA based face recognition using simulink (2014)

15.

Reinders, M.J., Koch, R., Gerbrands, J.J.: Locating facial features in image sequences using neural networks. In: Proceedings of the Second International Conference on Automatic Face and Gesture Recognition, pp. 230–235. IEEE (1996)

16.

Schilling, F.: The effect of batch normalization on deep convolutional neural networks (2016)

17.

Siswanto, A.R.S., Nugroho, A.S., Galinium, M.: Implementation of face recognition algorithm for biometrics based time attendance system. In: 2014 International Conference on ICT For Smart Society (ICISS), pp. 149–154 (2014). https://doi.org/10.1109/ICTSS.2014.7013165

18.

Xu, B., Wang, N., Chen, T., Li, M.: Empirical evaluation of rectified activations in convolutional network. arXiv preprint arXiv:1505.00853 (2015)

19.

Yarlagadda, V., Koolagudi, S.G., Kumar, M., Donepudi, S.: Driver drowsiness detection using facial parameters and RNNS with LSTM. In: 2020 IEEE 17th India Council International Conference (INDICON), pp. 1–7. IEEE (2020)

20.

Zheng, J., Peng, J.: A novel pedestrian detection algorithm based on data fusion of face images. Int. J. Distrib. Sens. Netw. 15(5), 1550147719845276 (2019)MathSciNetCrossRef

Titel: A Novel Approach for Detecting Facial Key Points Using Convolution Neural Networks
verfasst von: Rishi Kakkar
Y. V. Srinivasa Murthy
Verlag: Springer International Publishing
Buch: Artificial Intelligence and Speech Technology
Print ISBN: 978-3-030-95710-0

Electronic ISBN: 978-3-030-95711-7

Copyright-Jahr: 2022
DOI: https://doi.org/10.1007/978-3-030-95711-7_50

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner