Call for Paper - November 2022 Edition
IJCA solicits original research papers for the November 2022 Edition. Last date of manuscript submission is October 20, 2022. Read More

Recognizing Faces with Partial Occlusion using Inpainting

Print
PDF
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Authors:
Vijayalakshmi A.
10.5120/ijca2017914570

Vijayalakshmi A.. Recognizing Faces with Partial Occlusion using Inpainting. International Journal of Computer Applications 168(13):20-24, June 2017. BibTeX

@article{10.5120/ijca2017914570,
	author = {Vijayalakshmi A.},
	title = {Recognizing Faces with Partial Occlusion using Inpainting},
	journal = {International Journal of Computer Applications},
	issue_date = {June 2017},
	volume = {168},
	number = {13},
	month = {Jun},
	year = {2017},
	issn = {0975-8887},
	pages = {20-24},
	numpages = {5},
	url = {http://www.ijcaonline.org/archives/volume168/number13/27944-2017914570},
	doi = {10.5120/ijca2017914570},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Face recognition as a biometric is gaining popularity as it is widely being used in surveillance as well as for security. One of the disadvantage of face recognition as a biometric is its low recognition rate when a part of face image is lost due to known or unknown reasons. Occlusion on faces can be caused with the knowledge of a user when the user is covering his face or part of the face on purpose. Occlusion can be categorized as sparse occlusion and dense occlusion. The capability of face recognition system achieves its goal if the occluded part can be recovered for recognition of faces. In this paper, a hybrid inpainting approach is followed to recover the lost region of a face. This approach increases the recognition rate of faces that are occluded. Experimental result on hybrid inpainting proves that the recognition rate on faces increases on comparison with existing methods on occluded faces.

References

  1. Criminisi A., Pérez P. and Toyama K., “Region filling and object removal by exemplar-based image inpainting”, IEEE Transactions on Image Processing, vol.13, no. 9, pp.1200-1212, 2004.
  2. Viola P. and Jones M., “Rapid object detection using a boosted cascade of simple features”, in Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition,vol. 1, pp. 511-518, 2001.
  3. Rowley H., Baluja S. and Kanade T., “Rotation invariant neural network-based face detection”, In Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 38-44, 1998.
  4. Schneiderman H., and Kanade T. “A statistical method for 3D object detection applied to faces and cars”, in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 746-751, 2000.
  5. Freund Y. and Schapire R.E., “A desicion-theoretic generalization of on-line learning and an application to boosting”, European Conference on Computational learning theory, Springer: Berlin Heidelberg, pp. 23-37, 1995.
  6. Meynet J., Popovici V. and Thiran J. “Fast face detection using adaboost”, No. EPFL-STUDENT-86954, 2003.
  7. Schapire R. E., “Explaining Adaboost”, in Empirical Inference, Springer Berlin Heidelberg, pp. 37-52, 2013.
  8. Zitnick C. L. and Kanade T., “A cooperative algorithm for stereo matching and occlusion detection”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 7, pp.675-684, 2000.
  9. Chen Z., Xu T. and Han Z., “Occluded face recognition based on the improved SVM and block weighted LBP”, in Proceedings of IEEE International Conference on Image Analysis and Signal Processing, pp. 118-122, 2011.
  10. Pan X., Chen X. and Men A., “Occlusion Handling Based on Particle Filter inSurveillance System”, in Proceedings of International Conference on Computer Modeling and Simulation IEEE, vol 1, pp.179-183, 2010.
  11. Wang Y., “An Analysis of the Viola-Jones face detection algorithm”, Image Processing on Line, vol. 4, pp.128-148, 2014.
  12. Cortes C. and Vapnik V., “Support-vector networks”, Machine learning, vol.20, no.3, pp.273-297,1995.
  13. Vincent O. R. and Folorunso O., “A descriptive algorithm for sobel image edge detection”, in Proceedings of Informing Science & IT Education Conference, vol. 40, pp. 97-107, 2009.

Keywords

Face recognition, Occlusion, Inpainting

Learn about the IJCA article correction policy and process
Dealing with any form of infringement.
‘Peer Review – A Critical Inquiry’ by David Shatz
Directly place requests for print/ hard copies of IJCA via Google Docs