Volume : 3, Issue : 3, JUL 2019

A MULTIMODAL BIOMETRIC SYSTEM FOR FACE RECOGNITION: A SURVEY

J. Gowri

Abstract

Biometric credential system, which uses physical or behavioral features to check a person’s identity, ensures much greater security than passwords and number systems. The largest tentative research to date that analysis the combination and comparison of 2D and 3D face recognition. According to awareness, this is also the only such research to include noble time lapse between gallery and image acquisition, and to view at the effect of depth resolution. Recognition outcomes are acquired in single probe and a single gallery research, and a multiple probe and single gallery research. A total of 275 subjects contributed in one or more data acquisition sessions. Results are presented in gallery and analysis the discrete images of 200 in both 3D and 2D, with one to thirteen weeks’ time lapse between gallery and probe images of a certain subject squashy 951 pairs of 2D and 3D images. Using a PCA-based method refrained independently for 2D and for 3D, also it discovered that 3D outperforms 2D. This paper invented a multi-modal rank-one recognition amount of 98.5 percentage in a single probe research and 98.8 percentage in a multi-probe research, which is statistically considerably greater than either 2D or 3D alone.

Keywords

Biometrics, Face Recognition, Multi-Modal.

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References

  1. Zhao, R. Chellappa, A. Rosenfeld, and P. Phillips, “Face recognition: A literature survey,” To appear in ACM Computing Surveys.
  2. Kamel, H. Shen, A. Wong, T. Hong, and R. Campeanu, “Face recognition using perspective invariant features,” Pat-tern Recognition Letters, vol. 15, pp. 877–883, 1994.
  3. Wiskott, J-M. Fellous, N. Kruger, and C. Malsburg, “Face recognition by elastic bunch graph matching,” IEEE Trans.Pattern Anal. And Mach. Intel., vol. 19, no. 7, pp. 775–779, July 1997.
  4. Lee, B. Moghaddam, H. Pfister, and R.Machiraju, “Silhouette-based 3D face shape recovery,” Graphics Inter-face.
  5. Bronstein, M. Bronstein, and R. Kimmel, “Expression-invariant 3D face recognition,” Audio and Video based Bio-metric Person Authetication, 2003.
  6. Huang, B. Heisele, and V. Blanz, “Component-based face recognition with 3D morphable models,” Audio and Videobased Biometric Person Authetication, 2003.
  7. Chua, F. Han, and Y. Ho, “3D human face recognition using point signature,” Int’l Conf. on Automatic Face andGesture Recognition, pp. 233–238, 2000.
  8. Achermann, X. Jiang, and H. Bunke, “Face recognition using range images,” in Proceedings Int’l Conf. on VirtualSystems and MultiMedia ’97, Geneva, Switzerland, Sept.1997, pp. 129–136.
  9. Wang, C. Chua, and Y. Ho, “Facial feature detection and face recognition from 2D and 3D images,” Pattern Recognition Letters, vol. 23, pp. 1191–1202, 2002.