Face Matching Based on SIFT Algorithm in Campus Scenarios

Main Article Content

Wei Wu
Zhongwei, Liu
Kao Liu
Jinyan Li
Mingtao Pan
Hang Yang


 Face matching is an important technology in the field of computer vision and image recognition, face matching technology has been widely used in many fields, face matching will also be affected by the complexity of its lighting conditions, angles and other environments to affect the accuracy of face recognition, and the face recognition methods applied to face recognition can be divided into two types: based on local features and based on global features. Based on the learning of the SIFT algorithm, this paper proposes research on face matching based on SIFT algorithm in the campus environment, and conducts face matching experiments through MATLAB programming Under different lighting conditions, the feature points of the image in the face library are matched with the image to be matched. The results show that under normal lighting, face matching can be well realized, and the success rate of face matching gradually decreases under the condition that the light gradually dims, and even the face matching is unsuccessful. The SIFT algorithm can better identify the feature points extracted by the SIFT algorithm when the same face image is matched with different face images.

Article Details

How to Cite
Wu, W., Liu, Z., Liu, K., Li, J., Pan, M., & Yang, H. (2024). Face Matching Based on SIFT Algorithm in Campus Scenarios. Journal of Research in Multidisciplinary Methods and Applications, 3(4), 01240304001. Retrieved from http://www.satursonpublishing.com/jrmma/article/view/a01240304001


R. Brunelli and T. Poggio, Face recognition: Features versus templates, IEEE Transactions on Pattern Analysis and Machine Intelligence, 1993, 15(10): 1042-1052.

M. Turk, A. Pentland, Eigenfaces for recognition, Journal of Cognitive Neuroscience, 1991, 3 (1): 71-86.

P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman, Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection, IEEE Transactions on Pattern

M. S. Bartlett, J. R. Movellan, T. J. Sejnowski, Face recognition by independent component analysis, IEEE Transactions on Neural Networks, 2002, 13(6): 1450-1464.Analysis and Machine Intelligence, 1997, 19 (7): 711-720.

B. Moghaddam, A. Pentland, Probabilistic Visual Learning for Object Representation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19 (7): 696-710.

B. Moghaddam, T. Jebara, A. Pentland, Bayesian face recognition, Pattern Recognition, 2000, vol. 33, pp: 1771-1782.

M. Lades, J. C. Vorbruggen, J. Buhmann, J. Lange, C. von der Malsburg et al., Distortion invariant object recognition in the dynamic link architecture, IEEE Transactions on Computers, 1993, 42 (3): 300-311.

L. Wiskott, J. Fellous, N. Kruger, and C. von der Malsburg, Face recognition by elastic bunch graph matching, IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19 (7): 775-779.

Tang Dejun. Research on image feature extraction and matching technology in face recognition[D].Dalian Maritime University,2014.

Yang Limin. Research on image feature point localization algorithm and its application[D].Shanghai Jiao Tong University,2008.

Yang Yan. Research on image feature analysis and matching method of complex scene[D].Dalian University of Technology,2020.

Lowe, David G. "Distinctive Image Features from Scale-Invariant Keypoints." International Journal of Computer Vision 60, no. 2 (November 2004): 91--1191--110.

Most read articles by the same author(s)

1 2 > >>