A COMPARATIVE STUDY OF FACIAL RECOGNITION SYSTEMS
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Abstract
In human body, face plays an important role in social interactions and for recognizing people’s identity. Using the human face as a security tool, biometric face recognition technology has received significant interest in the past decade due to its likelyapplication for a wide variety of areas.Facial recognition system is an algorithm which identifies or verifies an individual by image acquisition and then mapping it to the face currently present in the database. This paper aims to provide a comparative study of various facial recognition systems built as of now.The motivation behind this paper is to provide researchers an insight into the current scenario of various facial recognition techniques employed to build distinct facial recognition systems, which have different accuracy and performance levels, in an unconstrained environment.Â
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References
Alshamsi, H., Kepuska, V., & Meng, H. (2017). Automated facial expression recognition app development on smart phones using cloud computing. 2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON). doi:10.1109/uemcon.2017.8249000
Bhattacherjee, T., Mukherjee, D., & Subashini, M. M. (2017). Low cost and processor efficient facial recognition system. 2017 Innovations in Power and Advanced Computing Technologies (i-PACT). doi:10.1109/ipact.2017.8245036
Cole, O., & El-Khatib, K. (2017). A Privacy Enhanced Facial Recognition Access Control System Using Biometric Encryption. 2017 13th International Conference on Distributed Computing in Sensor Systems (DCOSS). doi:10.1109/dcoss.2017.19.
Deng, W., Chen, B., Fang, Y., & Hu, J. (2017). Deep Correlation Feature Learning for Face Verification in the Wild. IEEE Signal Processing Letters, 24(12), 1877-1881. doi:10.1109/lsp.2017.2726105.
Face recognition a deep learning approach - TAU. (n.d.). Retrieved February 23, 2018, from http://www.bing.com/cr?IG=21B48D52EBCF44B798ABCABF91058B62&CID=13F5198C44D967F9184C1213457666B0&rd=1&h=drbxUGKvTzFcuGyNx27UBSB0TCjwd5EqrxAdj06PSCo&v=1&r=http%3a%2f%2fweb.eng.tau.ac.il%2fdeep_learn%2fwp-content%2fuploads%2f2016%2f11%2fFace-Recognition-A-Deep-Learning-Approach.pdf&p=DevEx,5103.1.
Hatami, N., Ebrahimpour, R., & Ghaderi, R. (2008). ECOC-based training of neural networks for face recognition. 2008 IEEE Conference on Cybernetics and Intelligent Systems. doi:10.1109/iccis.2008.4670763.
Huber, M. F., Merentitis, A., Heremans, R., Niessen, M., Debes, C., & Frangiadakis, N. (2016). Bayesian score level fusion for facial recognition. 2016 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI).
Juhong, A., & Pintavirooj, C. (2017). Face recognition based on facial landmark detection. 2017 10th Biomedical Engineering International Conference (BMEiCON). doi:10.1109/bmeicon.2017.8229173.
Sai, M. Y., Prasad, R. V., Niveditha, P. R., Sasipraba, T., Vigneshwari, S., & Gowri, S. (2017). Low cost automated facial recognition system. 2017 Second International Conference on Electrical, Computer and Communication Technologies (ICECCT). doi:10.1109/icecct.2017.8117829.
Senan, M. F., Abdullah, S. N., Kharudin, W. M., & Saupi, N. A. (2017). CCTV quality assessment for forensics facial recognition analysis. 2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence. doi:10.1109/confluence.2017.7943232.
Stanciu, L., & Blidariu, F. (2017). Emotional states recognition by interpreting facial features. 2017 E-Health and Bioengineering Conference (EHB). doi:10.1109/ehb.2017.7995414.
Suryaprasad, J., Sandesh, D. S., Priyanka, I., Pravalika, G. N., & Kumar, A. (2016). Parallel implementation and performance evaluation of facial recognition algorithms using open source technologies. 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT). doi:10.1109/iceeot.2016.7754972.
Taigman, Y., Yang, M., Ranzato, M., & Wolf, L. (2014). DeepFace: Closing the Gap to Human-Level Performance in Face Verification. 2014 IEEE Conference on Computer Vision and Pattern Recognition. doi:10.1109/cvpr.2014.220.
Turk, M., & Pentland, A. (n.d.). Face recognition using eigenfaces. Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. doi:10.1109/cvpr.1991.139758Technology/3rd Intl Conf on Computational Science/Intelligence and Applied Informatics/1st Intl Conf on Big Data, Cloud Computing, Data Science & Engineering (ACIT-CSII-BCD). doi:10.1109/acit-csii-bcd.2016.073.
Verma, G., Jindal, A., Gupta, S., & Kaur, L. (2017). A technique for face verification across age progression with large age gap. 2017 4th International Conference on Signal Processing, Computing and Control (ISPCC). doi:10.1109/ispcc.2017.8269749.
Zhang, H., & Jiang, C. (2017). Unconstrained face verification by subspace similarity metric learning. 2017 12th IEEE Conference on Industrial Electronics and Applications (ICIEA). doi:10.1109/iciea.2017.8282835.