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Fake Data Mining over Distributed Database With Face Annotation

Pankaj S Wankhede1 , Sachin Choudhari2 , Ashish Kumbhare3

Section:Research Paper, Product Type: Journal Paper
Volume-07 , Issue-12 , Page no. 81-84, May-2019

Online published on May 12, 2019

Copyright © Pankaj S Wankhede, Sachin Choudhari, Ashish Kumbhare . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Pankaj S Wankhede, Sachin Choudhari, Ashish Kumbhare, “Fake Data Mining over Distributed Database With Face Annotation,” International Journal of Computer Sciences and Engineering, Vol.07, Issue.12, pp.81-84, 2019.

MLA Style Citation: Pankaj S Wankhede, Sachin Choudhari, Ashish Kumbhare "Fake Data Mining over Distributed Database With Face Annotation." International Journal of Computer Sciences and Engineering 07.12 (2019): 81-84.

APA Style Citation: Pankaj S Wankhede, Sachin Choudhari, Ashish Kumbhare, (2019). Fake Data Mining over Distributed Database With Face Annotation. International Journal of Computer Sciences and Engineering, 07(12), 81-84.

BibTex Style Citation:
@article{Wankhede_2019,
author = {Pankaj S Wankhede, Sachin Choudhari, Ashish Kumbhare},
title = {Fake Data Mining over Distributed Database With Face Annotation},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {5 2019},
volume = {07},
Issue = {12},
month = {5},
year = {2019},
issn = {2347-2693},
pages = {81-84},
url = {https://www.ijcseonline.org/full_spl_paper_view.php?paper_id=1049},
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
UR - https://www.ijcseonline.org/full_spl_paper_view.php?paper_id=1049
TI - Fake Data Mining over Distributed Database With Face Annotation
T2 - International Journal of Computer Sciences and Engineering
AU - Pankaj S Wankhede, Sachin Choudhari, Ashish Kumbhare
PY - 2019
DA - 2019/05/12
PB - IJCSE, Indore, INDIA
SP - 81-84
IS - 12
VL - 07
SN - 2347-2693
ER -

           

Abstract

A face annotation has many applications the main part of based face annotation is to management of most same facial images and their weak data labels. This problem different method are adopted. The efficiency of annotating systems are improved by using these methods. This paper proposes a review on various techniques used for detection and analysis of each technique. Combine techniques are used in retrieving facial images based on query. So it is effective to label the images with their exact names. The detected face recognition techniques can annotate the faces with exact data labels which will help to improve the detection more efficiently. For a set of semantically similar images Annotations from them. Then content-based search is performed on this set to retrieve visually similar images, annotations are mined from the data descriptions. The method is to find the face data association in images with data label. Specifically, the task of face-name association should obey the constraint face can be a data appearing in its associated a name can be given to at most one face and a face can be assigned to one name.

Key-Words / Index Term

Annotation, weak data, exact data, detection, Content Based

References

[1] Mining Weakly Labeled Web Facial Images for Search-Based Face Annotation Dayong Wang, Steven C.H. Hoi, Member, IEEE, Ying He,
and Jianke Zhu JANUARY 2017.
[2] Dayong Wang, Steven C.H. Hoi, Ying He, and Jianke Zhu,”
Mining Weakly Labeled Web Facial Images for Search-Based
Face Annotation” IEEE Transactions on Knowledge and Data
Engineering, vol. 26, no. 1, January 2014
[3] D. Wang, S.C.H. Hoi, Y. He, and J. Zhu, “Retrieval-Based Face Annotation by Weak Label Regularized Local Coordinate Coding,” Proc. 19th ACM Int’l Conf. Multimedia (Multimedia), pp. 353-362, 2011.
[4] W. Dong, Z. Wang, W. Josephson, M. Charikar, and K. Li, “Modeling LSH for Performance Tuning,” Proc. 17th ACM Conf. Information and Knowledge Management (CIKM), pp. 669-678, 2008
[5] C. Siagian and L. Itti, “Rapid Biologically-Inspired Scene Classification Using Features Shared with Visual Attention,”IEEE Trans. Pattern Analysis and Machine Intelligence, vol.29, no. 2,pp. 300-312, Feb. 2007.
[6] Y. Tian, W. Liu, R. Xiao, F. Wen, and X. Tang, “A Face Annotation Framework with Partial Clustering and Interactive Labeling,” Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2007.
[7] X.-J. Wang, L. Zhang, F. Jing, and W.-Y. Ma, “AnnoSearch: Image Auto-Annotation by Search,” Proc. IEEE CS Conf. Computer Vision and Pattern Recognition (CVPR), pp. 1483- 1490, 2006.
[8] W. Zhao, R. Chellappa, P.J. Phillips, and A. Rosenfeld, “Face Recognition: A Literature Survey,” ACM Computing Survey, vol. 35, 2003.
[9] A.W.M. Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain, “Content-Based Image Retrieval at the End of the Early Years,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 12, pp. 1349-1380, Dec. 2000.