Open Access
Neha Shrotriya
Veena Yadav
Geeta Tiwari
Shilpa Kalra
- Assistant Professor Poornima College of Engineering, India Rajasthan India
- Associate Professor Poornima College of Engineering, India Rajasthan India
- Assistant Professor Poornima College of Engineering, India Rajasthan India
- Assistant Professor Poornima College of Engineering, India Rajasthan India
Abstract
Composite materials can indeed play a crucial role in various phases of a face recognition system, offering advantages such as lightweight construction, durability, and tailored mechanical properties. Let’s explore how composite materials can be utilized in different phases of a face recognition system. A composite material based different phases of Face recognitions System is software that recognizes or verifies a person based on a digital image or a frame from a video source. We investigated a facial recognition system using support vector machines (SVM), a type of machine learning, in this review. Advanced composites are widely favored across various engineering applications due to their exceptional specific strength and specific stiffness, offering superior performance relative to their weight. Within the aircraft and aerospace industries, high-strength fibers such as carbon, glass, and Kevlar are commonly employed. We compared various facial composite material based different phases of facial recognitions System using a global method of feature extraction based on Histogram-Oriented Gradient. Face detection is accomplished using Convolutional Neural Networks, a subset of Deep Learning (CNN). It is a multi-layered structure that has been taught to use categorization to perform a specific task. Our study provides a thorough introduction to face detection, as well as various approaches, computer vision fundamentals, and applications that will be useful in image processing and computer vision research
Keywords: Composite material, Face Recognition, SVM, CNN, HOG, Biometric Authentication
References
[1] Dambhare S, Deshmukh S, Borade A, Digalwar A, Phate M. Sustainability issues in turning process: A study in Indian machining Industry. Procedia CIRP. 2015 Jan 1;26:379-84..
[2] Wang X, Guo H, Hu S, Chang MC, Lyu S. GAN-generated Faces Detection: A Survey and New Perspectives. 2022.
[3] Petrescu RV. Face recognition as a biometric application. Journal of Mechatronics and Robotics. 2019 Apr 13;3:237-57.
[4] Smith M, Miller S. The ethical application of biometric facial recognition technology. AI Soc. 2022;37:167-175.
[5] Li L, Mu X, Li S, Peng H. A Review of Face Recognition Technology. IEEE Access. 2020.
[6] Nair PG, R S. A Review: Facial Recognition Using Machine Learning. IRJET J. 2020.
[7] Pandey IR, Raj M, Sah KK, Mathew T, Padmini MS. Face Recognition Using Machine Learning. Int J Recent Eng Sci (IJRES). 2019.
[8] Ranjan Nath R, Kakoty K, Bora DJ. Face Detection and Recognition Using Machine LearningAlgorithm.2021.Availablefrom:https://www.researchgate.net/publication/348917290.
[9] Alvappillai A, Barrina PN. Face Recognition using Machine Learning.
[10] Akanksha, Kaur J, Singh H. Face detection and Recognition: A review. 2018.
[11] Doberstein C, Charbonneau É, Morin G, Despatie S. Measuring the Acceptability of Facial Recognition-Enabled Work Surveillance Cameras in the Public and Private Sector. 2021. Available from: https://www.tandfonline.com/loi/mpmr20.
[12] Bansal A, Agarwal M, Sharma A, Gupta A. A Review Paper on FACIAL RECOGNITION. Int J Recent Innov Trends Comput Commun. 2013.
[13] Kim S, An GH, Kang S. Facial expression recognition system using machine learning. Int SoC Des Conf (ISOCC), Seoul. 2017.
[14] García Amaro E, Nuño-Maganda MA, Morales-Sandoval M. Evaluation of machine learning techniques for face detection and recognition. CONIELECOMP 22nd Int Conf Electr Commun Comput, Cholula, Puebla. 2012.
[15] Sang DV, Van Dat N, Thuan DP. Facial expression recognition using deep convolutional neural networks. 9th Int Conf Knowl Syst Eng (KSE), Hue. 2017.
[16] Vinay A. Face recognition using interest points and ensemble of classifiers. 4th Int Conf Recent Adv Inf Technol (RAIT), Dhanbad. 2018.
[17] Liu Q, Li P, Zhao W, Cai W, Yu S, Leung VCM. A Survey on Security Threats and Defensive Techniques of Machine Learning: A Data-Driven View. IEEE Access. 2018;6:12103-12117.
[18] Bai XF, Wang WJ. An approach for facial expression recognition based on neural network ensemble. Int Conf Mach Learn Cybern, Hebei. 2009.
[19] Ishii M. Basic research on facial expression recognition model with adaptive learning capability. IEEE Int Conf Syst Man Cybern, Anchorage, AK. 2011.
[20] Mullainathan S, Spiess J. Machine Learning: An Applied Econometric Approach. J Econ Perspect. 2017;31:87-106.
[21] Kundu T, Saravanan C. Advancements and recent trends in emotion recognition using facial image analysis and machine learning. Int Conf Electr Electron Commun Comput Optim Tech (ICEECCOT), Mysuru. 2017.
[22] Mostafa A, Khalil MI, Abbas H. Emotion Recognition by Facial Features using Recurrent Neural Networks. 13th Int Conf Comput Eng Syst (ICCES), Cairo. 2018.
[23] Coşkun M, Uçar A, Yıldırım Ö, Demir Y. Face Recognition Based on Convolutional Neural Network. 2017
Journal of Polymer and Composites
| Volume | |
| Received | January 23, 2024 |
| Accepted | April 23, 2024 |
| Published | May 31, 2024 |