A Review: Facial Recognition Using Machine Learning

A Review: Facial Recognition Using Machine Learning

  IJRES-book-cover  International Journal of Recent Engineering Science (IJRES)          
  
© 2020 by IJRES Journal
Volume-7 Issue-3
Year of Publication : 2020
Authors : Pooja G Nair, Sneha R
DOI :    10.14445/23497157/IJRES-V7I3P115

Citation 

MLA Style :Pooja G Nair, Sneha R  "A Review: Facial Recognition Using Machine Learning" International Journal of Recent Engineering Science 7.3(2020):85-89. 

APA Style :Pooja G Nair, Sneha R. A Review: Facial Recognition Using Machine Learning International Journal of Recent Engineering Science, 7(3),85-89.

Abstract
A facial recognition system can verify or identify a person from a video or a digital image. There are various techniques in which these systems work. Popularly, they work by first matching the facial characteristics picked from the image to the faces stored in the database. It is called a Biometric Identification based application that uniquely identifies each individual by analyzing their voice, facial expression, face, or fingerprint. Even though it was initially used as a computer application, it has gained broader uses in mobile platforms and other technology sectors, such as robotics. It has a vast application in security systems. Although this system`s accuracy as biometric technology is lower than that of fingerprint recognition and iris detection, it is broadly used due to its non-invasive and contactless features. It has recently grown in significance as a tool for retail and marketing. Another application is video surveillance to identify missing people or criminals. It is gaining importance in the healthcare sector. Facial recognition technology has become very popular and is being used everywhere from shopping centers, airports, venues, and by law enforcement. This technology can also be used to prevent crimes such as shoplifting by identifying ex-cons. Although this technology is gaining widespread use, there are many concerns about privacy and safety.

Reference
[1] S. Kim, G. H. An and S. Kang, “Facial expression recognition system using machine learning”, 2017 International SoC Design Conference (ISOCC), Seoul, 2017.
[2] E. García Amaro, M. A. Nuño-Maganda and M. MoralesSandoval, “Evaluation of machine learning techniques for face detection and recognition”, CONIELECOMP 2012, 22nd International Conference on Electrical Communications and Computers, Cholula, Puebla, 2012.
[3] D. V. Sang, N. Van Dat, and D. P. Thuan, “Facial expression recognition using deep convolutional neural networks”, 2017 9th International Conference on Knowledge and Systems Engineering (K.S.E.), Hue, 2017.
[4] A. Vinay, “Face recognition using interest points and ensemble of classifiers”, 2018 4th International Conference on Recent Advances in Information Technology (RAIT), Dhanbad, 2018,
[5] Q. Liu, P. Li, W. Zhao, W. Cai, S. Yu, and V. C. M. Leung, “A Survey on Security Threats and Defensive Techniques of Machine Learning: A Data-Driven View”, IEEE Access, vol. 6, pp. 12103- 12117, 2018.
[6] Xue-Fei Bai and Wen-Jian Wang, “An approach for facial expression recognition based on neural network ensemble”, 2009 International Conference on Machine Learning and Cybernetics, Hebei, 2009.
[7] M. Ishii, “Basic research on facial expression recognition model with adaptive learning capability”, 2011 IEEE International Conference on Systems, Man, and Cybernetics, Anchorage, AK, 2011.
[8] Mullainathan Sendhil and Spiess Jann, “Machine Learning: An Applied Econometric Approach”, Journal of Economic Perspectives, Volume 31 87-106, May 2017.
[9] T. Kundu and C. Saravanan, “Advancements and recent trends in emotion recognition using facial image analysis and machine learning models”, 2017 International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques (ICEECCOT), Mysuru, 2017.
[10] A. Mostafa, M. I. Khalil, and H. Abbas, “Emotion Recognition by Facial Features using Recurrent Neural Networks”, 2018 13th International Conference on Computer Engineering and Systems (ICCES), Cairo, Egypt, 2018.
[11] Co?kun, Musab & Uçar, Ay?egül & y?ld?r?m, Özal & Demir, Yakup, “Face Recognition Based on Convolutional Neural Network”, 2017.
[12] J. C. T. Kwong, F. C. C. Garcia, P. A. R. Abu, and R. S. J. Reyes, “Emotion Recognition via Facial Expression: Utilization of Numerous Feature Descriptors in Different Machine Learning Algorithms”, TENCON 2018 - 2018 IEEE Region 10 Conference, Jeju, Korea (South), 2018.
[13] X. Han and Q. Du, “Research on face recognition based on deep learning”, 2018 Sixth International Conference on Digital Information, Networking, and Wireless Communications (DINWC), Beirut, 2018.
[14] Farhad Navabifar, Mehran Emadi, Rubiyah Yusof and Marzuki Khalid, “A short review paper on Face detection using Machine learning”, 2011.
[15] Stephen Balaban, “Deep learning and face recognition: the state of the art”, 2015.
[16] Bhumika Pathya, Sumita Nainan, “Performance Evaluation of Face Recognition using LBP, P.C.A. and SVM” SSRG International Journal of Computer Science and Engineering 3.4 (2016)

Keywords
Convoluted Neural Networks, Facial Recognition, Machine Learning, Support Vector Machine