FACE RECOGNITION WITH HYBRID TECHNIQUES
DOI:
https://doi.org/10.29121/ijetmr.v5.i2.2018.642Keywords:
Framework, Face Recognition, Deep Learning, PreprocessingAbstract
Face recognition framework is still in test by numerous applications particularly in close perception and in security frameworks. Generally all utilizations of face recognition utilize enormous information sets, making challenges in present time preparing and effectiveness. This paper contains a structure to enhance face recognition framework which have a few phases. For good result in face recognition framework a few upgrades are critical at each stage. A novel plan is displayed in this paper which gives the better execution for face recognition framework. This plan incorporates expanding in datasets, particularly huge datasets which are required for profound learning. Changing the picture differentiate proportion and pivoting the picture at a few edges which can enhance the recognition precision. At that point, trimming the proper territory of face for highlight extraction and getting the best element vector for face recognition finally. The last after effect of this plan will demonstrate that the given structure is able for distinguishing and perceiving faces with various postures, foundations, and appearance in genuine or present time.
Downloads
References
Kwak K C, Pedrycz W. Face recognition using a fuzzy fisherface classifier [J]. Pattern Recognition, 2005, 38(10): 1717-1732. DOI: https://doi.org/10.1016/j.patcog.2005.01.018
Wang X, Han T X, Yan S. An HOG-LBP human detector with partial occlusion handling [C]. Computer Vision, 2009 IEEE 12th International Conference on. IEEE, 2009: 32-39. DOI: https://doi.org/10.1109/ICCV.2009.5459207
Sun Y, Wang X, Tang X. Deeply learned face representations are sparse, selective, and robust [C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015: 2892-2900. DOI: https://doi.org/10.1109/CVPR.2015.7298907
Xu Y, Zhang Z, Lu G, et al. Approximately symmetrical face images for image preprocessing in face recognition and sparse representation based classification[J]. Pattern Recognition, 2016, 54: 68-82. DOI: https://doi.org/10.1016/j.patcog.2015.12.017
AbdAlmageed W, Wua Y, Rawlsa S, et al. Face recognition using deep multi-pose representations [J]. arXiv preprint arXiv:1603.07388, 2016.
Taigman Y, Yang M, Ranzato M A, et al. Deepface: Closing the gap to human-level performance in face verification [C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2014: 1701-1708. DOI: https://doi.org/10.1109/CVPR.2014.220
Parkhi O M, Vedaldi A, Zisserman A. Deep face recognition [J]. Proceedings of the British Machine Vision, 2015, 1(3): 6 DOI: https://doi.org/10.5244/C.29.41
Downloads
Published
How to Cite
Issue
Section
License
License and Copyright Agreement
In submitting the manuscript to the journal, the authors certify that:
- They are authorized by their co-authors to enter into these arrangements.
- The work described has not been formally published before, except in the form of an abstract or as part of a published lecture, review, thesis, or overlay journal.
- That it is not under consideration for publication elsewhere.
- That its release has been approved by all the author(s) and by the responsible authorities – tacitly or explicitly – of the institutes where the work has been carried out.
- They secure the right to reproduce any material that has already been published or copyrighted elsewhere.
- They agree to the following license and copyright agreement.
Copyright
Authors who publish with International Journal of Engineering Technologies and Management Research agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC BY-SA 4.0) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors can enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or edit it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) before and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
For More info, please visit CopyRight Section