International Journal of Advanced Networking and Applications

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Facial Feature Detection in Real-Time: A Novel Approach with MTCNN Deep Learning


Affiliations
1 Department of Computer Science, Punjabi University Patiala, India
2 Head of Department of Computer Science, Guru Hargobind Sahib Khalsa Girls College Karhali Sahib, Patiala, India
 

In this study, we present an innovative approach to real-time facial feature detection utilizing the MTCNN (Multi- Task Cascaded Neural Network) deep learning architecture. Unlike traditional methods, our novel framework combines advanced techniques to achieve unparalleled precision and efficiency in facial feature localization. Through a meticulous exploration of MTCNN's capabilities, we unveil a transformative methodology that significantly enhances the speed and accuracy of real-time facial detection. Our research focuses on pushing the boundaries of existing facial recognition technologies, introducing a fresh perspective on the application of MTCNN. By leveraging its unique architecture, we not only address the challenges associated with real-time detection but also enhance the overall robustness of the system. The proposed approach showcases the untapped potential of MTCNN, establishing it as a key player in the realm of facial feature detection. Through rigorous experimentation and evaluation, we demonstrate the superiority of our approach over conventional methods, highlighting its effectiveness in diverse scenarios. This work contributes to the ongoing evolution of deep learning applications in computer vision, with implications for security, surveillance, and various human-computer interaction domains. Our findings open new avenues for researchers and practitioners seeking cutting-edge solutions in the dynamic field of real-time facial feature detection.

Keywords

MTCNN, Face Detection, Machine Learning, Deep Learning, Real Time.
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  • . Hung B.T, Kumar R., Quang N.H., Kumar Solanki V., Cardona M., Pattnaik P.K (2021) “Face Recognition Using Hybrid HOG-CNN Approach”, Research in Intelligent and Computing in Engineering. Advances in Intelligent Systems and Computing, vol 1254. Springer, Singapore. https://doi.org/10.1007/978-981- 15-7527-3_67.

  • . M. Seshaiah, Shrishail (2021) “Comparative Analysis of Various Face Detection and Tracking and Recognition Mechanismsusing Machine and Deep Learning Methods”, Turkish Journal of Computer and Mathematics Education Vol.12 No. 11, 215-223.

  • . L. Zhang, H. Wang and Z. Chen(2021) "A Multi-task Cascaded Algorithm with Optimized Convolution Neural Network for Face Detection", Asia-Pacific Conference on Communications Technology and Computer Science (ACCTCS), pp. 242-245, doi: 10.1109/ACCTCS52002.2021.00054.

  • . Soleen Basim Mohammed , Adnan MohsinAbdulazeez. (2021). Deep Convolution Neural Network for Facial Expression Recognition. PalArch’s Journal of Archaeology of Egypt / Egyptology, 18(4),3578- 3586.https://archives.palarch.nl/index.php/jae/article/vie w/6874.

  • . Xiang Li, Jianzheng Liu, Jessica Baron, KhoaLuu& Eric Patterson (2021).” Evaluating effects of focal length and viewing angle in a comparison of recent face landmark and alignment methods” Journal Image Video Proc. https://doi.org/10.1186/s13640-021-00549-3.

  • . R. Guha, "A Report on Automatic Face Recognition: Traditional to Modern Deep Learning Techniques," 6th International Conference for Convergence in Technology (I2CT), pp. 1-6,2021.doi: 10.1109/I2CT51068.2021.9418068.

  • . F. M. J. MehediShamrat, M. A. Jubair, M. M. Billah, S. Chakraborty, M. Alauddin, and R. Ranjan, "A Deep Learning Approach for Face Detection using Max Pooling," 2021 5th International Conference on Trends in Electronics and Informatics (ICOEI), pp.760- 764,2021. DOI: 10.1109/ICOEI51242.2021.9452896.

  • . Zhao, D., Qian, Y., Liu, J., "The facial expression recognition technology under image processing and neural network”, J Supercomput, 2021. https://doi.org/10.1007/s11227-021-04058-y.

  • . Showkat A. Dar, and S.Palanivel, “Neural Networks (CNNs) and Vgg on Real-Time Face Recognition System ", Turkish Journal of Computer and Mathematics Education, Vol.12 No.9 pp. 1809- 1822,2021. https://doi.org/10.17762/turcomat.v12i9.3606.

  • .Tuba E. Alkhan, Alaa A. Hameed,A. Jamil,“Deep Learning for Face Detection and Recognition”, International Conference on Advanced Engineering, Technology and Applications,Istanbul, Turkey, 2021.

  • . Said, E., & Nasr, M. (2020). Face recognition system. International Advanced Networking and Applications, 12(02), 4567–4574. https://doi.org/10.35444/ijana.2020.12205.

  • . Mona Nasr, Omar Farouk, Ahmed Mohamedeen, Ali Elrafie, Marwan Bedeir, Ali Khaled, Benchmarking Metaheuristic Optimization, International Journal of Advanced Networking and Applications (IJANA), Volume 11 Issue 6 Pages: 4451-4457 (2020).

  • . Farrag, M., Nasr, M., A Proposed Algorithm to Detect the Largest Community Based on Depth Level, International Journal of Advanced Networking and Applications (IJANA), Volume 09, Issue 02, Sep - Oct 2017 issue, pp. 3362-3375.

  • . Mona Nasr, Rana Osama, Rana Osama, Nouran Mosaad, Nourhan Ebrahim, Adriana Mounir, Realtime Multi-Person 2D Pose Estimation, International Journal of Advanced Networking and Applications (IJANA), Volume 11 Issue 6 Pages: 4501-4508 (2020).


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  • Facial Feature Detection in Real-Time: A Novel Approach with MTCNN Deep Learning

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Authors

Honey
Department of Computer Science, Punjabi University Patiala, India
Sukhwinder Singh Oberoi
Head of Department of Computer Science, Guru Hargobind Sahib Khalsa Girls College Karhali Sahib, Patiala, India

Abstract


In this study, we present an innovative approach to real-time facial feature detection utilizing the MTCNN (Multi- Task Cascaded Neural Network) deep learning architecture. Unlike traditional methods, our novel framework combines advanced techniques to achieve unparalleled precision and efficiency in facial feature localization. Through a meticulous exploration of MTCNN's capabilities, we unveil a transformative methodology that significantly enhances the speed and accuracy of real-time facial detection. Our research focuses on pushing the boundaries of existing facial recognition technologies, introducing a fresh perspective on the application of MTCNN. By leveraging its unique architecture, we not only address the challenges associated with real-time detection but also enhance the overall robustness of the system. The proposed approach showcases the untapped potential of MTCNN, establishing it as a key player in the realm of facial feature detection. Through rigorous experimentation and evaluation, we demonstrate the superiority of our approach over conventional methods, highlighting its effectiveness in diverse scenarios. This work contributes to the ongoing evolution of deep learning applications in computer vision, with implications for security, surveillance, and various human-computer interaction domains. Our findings open new avenues for researchers and practitioners seeking cutting-edge solutions in the dynamic field of real-time facial feature detection.

Keywords


MTCNN, Face Detection, Machine Learning, Deep Learning, Real Time.

References