Authors :
Alka Mishra; Akash Mishra; Vandna Pathak
Volume/Issue :
Volume 9 - 2024, Issue 7 - July
Google Scholar :
https://tinyurl.com/4zxvutj5
Scribd :
https://tinyurl.com/5ek9u574
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24JUL1016
Abstract :
The emerging field of "Smart Face
Recognition" utilizes IoT and machine learning to
accurately identify individuals based on their facial
characteristics. Various industries such as security, retail,
and healthcare are leveraging this technology to enhance
customer satisfaction and increase productivity. By
combining IoT and machine learning, large amounts of
data can be collected from multiple sources, such as
cameras and sensors, and used to train algorithms for
real-time, precise identification of individuals. This
technology is gaining popularity due to its accuracy,
speed, and scalability, making it essential for applications
like security and access control. Recognizing human facial
emotions is a key focus in today's technological landscape,
with robotic applications across various sectors
highlighting the importance of emotion recognition for
effective human-robot interaction. This project aims to
develop and implement a new automated system for
emotion detection and facial recognition using Artificial
Intelligence (AI) and the Internet of Things (IoT).
Keywords :
Face Recognition, Emotion Detection, Artificial Intelligence, and Internet of Things.
References :
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- [ZKC+98] W. Zhao, A. Krishnaswamy, R. Chellappa, D. Swets and J. Weng.Discriminant analysis of principal components for face recognition, pages 73-85. Springer Verlag Berlin, 1998.
- [GHW12] M. Günther, D. Haufe and R.P. Würtz. Face recognition with disparity corrected Gabor phase differences. In Artificial neural networks and machine learning, volume 7552 of Lecture Notes in Computer Science, pages 411-418. 9/2012.
- Khan, M. H. Javed, E. Ahmed, S. A. A. Shah and S. U. Ali, "Facial Recognition usingConvolutional Neural Networks and Implementation on Smart Glasses," 2019 International Conference on Information Science and Communication Technology (ICISCT), 2019, pp. 1-6, doi: 10.1109/CISCT.2019.8777442.
- Mehedi Masud, Ghulam Muhammad, Hesham Alhumyani, Sultan S Alshamrani, Omar Cheikhrouhou, Saleh Ibrahim, M. Shamim Hossain, Deep learning-based intelligent face recognition in IoT-cloud environment, Computer Communications, Volume 152, 2020, Pages 215-222, ISSN 0140- 3664.
- Bhatti, K., Mughal, L., Khuhawar, F., &Memon, S. (2018). Smart attendance management system using face recognition. EAI Endorsed Transactions on Creative Technologies, 5(17).
- Kumar, P. M., Gandhi, U., Varatharajan, R., Manogaran, G., & Vadivel, T. (2019). Intelligent face recognition and navigation system using neural learning for smart security in the Internet of Things. Cluster Computing, 22(4), 7733-7744.
- Agarwal, L., Mukim, M., Sharma, H., Bhandari, A., & Mishra, A. (2021, March). Face recognition based smart and robust attendance monitoring using deep CNN. In 2021 8th International Conference on Computing for Sustainable Global Development (INDIACom) (pp. 699-704). IEEE.
- Kumar, T. A., Rajmohan, R., Pavithra, M., Ajagbe, S. A., Hodhod, R., & Gaber, T. (2022). Automatic face mask detection system in public transportation in smart cities using IoT and deep learning. Electronics, 11(6), 904.
- Atik, M. E., & Duran, Z. (2020, October). Deep learning-based 3d face recognition using derived features from point cloud. In The Proceedings of the Third International Conference on Smart City Applications (pp. 797-808). Springer, Cham.
- BoserB ,Guyon I.G,Vapnik V., "A Training Algorithm for Optimal Margin Classifiers", Proc. Fifth Ann. Workshop Computational Learning Theory,pp. 144-152, 1992.
- Mitchell, T. (1997). Machine Learning, McGraw Hill. ISBN 0-07-042807-7., McGraw-Hill, Inc. New York, NY, USA. Published on March 1, 1997
- Alex C, Boston A. (2016).Artificial Intelligence, Deep Learning, and Neural Networks, Explained (16:n37)
- Varun G., Lily P., Mark C., “Development and validation of a deep learning Algorithm for Detection of Diabetic Retinopathy”, December 2016.
- Tiago T.G. “Machine Learning on the Diabetic Retinopathy Debrecen Dataset”, knowledge- Based System60, 20-27. Published on June 25, 2016.
- Yau JW, Rogers SL, Kawasaki R, Lamoureux EL, Kowalski JW, Bek T, et al. Global prevalence and major risk factors of diabetic retinopathy. Diabetes Care. 2012;35:556–64
- Boser B. E, Guyon I. M. and Vapnik V. N. (1992). “A training algorithm for optimal margin classiers”.Proceedings of the 5th Annual Workshop on Computational Learning Theory COLT'92, 152 Pittsburgh, PA, USA. ACM Press, July 1992. On Page(s): 144-152
The emerging field of "Smart Face
Recognition" utilizes IoT and machine learning to
accurately identify individuals based on their facial
characteristics. Various industries such as security, retail,
and healthcare are leveraging this technology to enhance
customer satisfaction and increase productivity. By
combining IoT and machine learning, large amounts of
data can be collected from multiple sources, such as
cameras and sensors, and used to train algorithms for
real-time, precise identification of individuals. This
technology is gaining popularity due to its accuracy,
speed, and scalability, making it essential for applications
like security and access control. Recognizing human facial
emotions is a key focus in today's technological landscape,
with robotic applications across various sectors
highlighting the importance of emotion recognition for
effective human-robot interaction. This project aims to
develop and implement a new automated system for
emotion detection and facial recognition using Artificial
Intelligence (AI) and the Internet of Things (IoT).
Keywords :
Face Recognition, Emotion Detection, Artificial Intelligence, and Internet of Things.