Abstract
Facial biometrics continues to be the preferred biometric bench-mark even though other human body signatures are also used. But there are many problems with face recognition. A slight change in the image, which may be due to illumination, or to the environment, can drastically affect the results and accuracy. These changes may not even be noticeable to the human eye, but can affect the face recognition process. There are some advantages, such as contactless, easy to use and, keeping in mind the current situation, reduces the risk of getting infected by touching buttons or by simply waiting in the queue for a long time. In this paper, an effort is made to review different kinds of face recognition methods comprehensively. PCA, LDA, SVM, and various hybrid techniques are used for the face recognition approach. This review examines all of the aforementioned techniques as well as various parameters that pose challenges to face recognition, such as illumination, pose variations, and facial expression.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
P. Buddharaju, I.T. Pavlidis, M. Bazakos, Physiology-based face recognition in the thermal infrared spectrum, in IEEE Conference on Advanced Video and Signal Based Surveillance (2007), pp. 354–359
S.A. Robila, Toward hyperspectral face recognition [6812-32], in Proceedings—SPIE The International Society For Optical Engineering, vol. 6812 (International Society for Optical Engineering, 1999, 2008), p. 6812
M. Turk, A. Pentland, Eigenfaces for recognition. J. Cogn. Neurosci. 3(1), 71–86 (1991)
M. Abdullah, M. Wazzan, S. Bo-Saeed, Optimizing face recognition using PCA (2012), arXiv preprint arXiv:1206.1515
X. Lu, Yagnik, A literature survey on face recognition techniques. Int. J. Comput. Trends Technol. 4(5) (2013)
F.J. Prokoski, R.B. Riedel, J.S. Coffin, Identification of individuals by means of facial thermography. Int. Carnahan Conf. Secur. Technol. 120–125 (1992)
X. Maldague, Theory and Practice of Infrared Technology for Nondestructive Testing (Wiley, New York, 2001)
R. Dhaya, Hybrid machine learning approach to detect the changes in SAR images for salvation of spectral constriction problem. J. Innovative Image Process. (JIIP) 3(02), 118–130 (2021)
S.T. Kumar, Study of retail applications with virtual and augmented reality technologies. J. Innovative Image Process. (JIIP) 3(02), 144–156 (2021)
T. Vijayakumar, Synthesis of palm print in feature fusion techniques for multimodal biometric recognition system online signature. J. Innovative Image Process. (JIIP) 3(02), 131–143 (2021)
A. Sungheetha, R. Sharma, 3D image processing using machine learning based input processing for man–machine interaction. J. Innovative Image Process. (JIIP) 3(01), 1–6 (2021)
G. Ranganathan, Real life human movement realization in multimodal group communication using depth map information and machine learning. J. Innovative Image Process. (JIIP) 2(02), 93–101 (2020)
Acknowledgements
I would like to thank to my teachers and Friends who helped me in completing this project. I would like to specially thanks to My Guide (Ms. Jaspreet Kaur) whose efforts, guidance and direction gives me a motivation to complete this manuscript.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Rana, W., Pandey, R., Kaur, J. (2022). Face Recognition in Different Light Conditions. In: Smys, S., Balas, V.E., Palanisamy, R. (eds) Inventive Computation and Information Technologies. Lecture Notes in Networks and Systems, vol 336. Springer, Singapore. https://doi.org/10.1007/978-981-16-6723-7_62
Download citation
DOI: https://doi.org/10.1007/978-981-16-6723-7_62
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-6722-0
Online ISBN: 978-981-16-6723-7
eBook Packages: EngineeringEngineering (R0)