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Mining Weakly Labeled Web Facial Images for Search-Based Face Annotation Using Neural Network Classifier

  • A. A. Kale
  • A. F. N. MullaEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1025)

Abstract

This paper inspects a structure for search-based-face annotation using mining weakly labeled web facial images. Facial photographs are candidly present on the Internet, from that a few facial photographs are properly labeled, however, some of them are not correctly labeled. These facial photographs are repeatedly incomplete and noisy. For enhancing tag quality of weakly net facial photographs, ULR approach is also advantageous for cleansing or filtering the tags of net facial photographs (Wang et al. in IEEE Trans Knowl Data Eng 26, [1]). Big headache issue for search-based face annotation scheme is, whenever the given test facial portrait is not a common person, there are no much more same facial photographs present on the web. A supervised appropriate name tag can be given to a test face portrait by employing face annotation using search-based paradigm, but it also increases the efficiency and scalability. The supervised neural network classifier approach is looking to optimize the tag quality of face portrait by majority voting against the face annotation by search-based paradigm.

Keywords

Face annotation Neural network classifier Mining Graphics and intelligence-based script technology 

References

  1. 1.
    Wang, D., Hoi, S.C.H., He, Y.: Mining weakly labeled web facial images for search-based face annotation. IEEE Trans. Knowl. Data Eng. 26(1) (2014)Google Scholar
  2. 2.
    Ozkan, D., Duygulu, P.: A graph based approach for naming faces in news photos. In: Proceedings of the IEEE CS Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1477–1482 (2006)Google Scholar
  3. 3.
    Berg, T.L., Berg, A.C., Forsyth, D.A.: Names and faces in the news. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 848–854 (2004)Google Scholar
  4. 4.
    Tian, Y., Liu, W., Xiao, R., Wen, F., Tang, X.: A face annotation framework with partial clustering and interactive labelling. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2007)Google Scholar
  5. 5.
    Smeulders, A., Worring, M., Santini, S., Gupta, A., Jain, R.: Content-based image retrieval at the end of the early years. IEEE Trans. PAMI 22(12), 1349–1380 (2000)Google Scholar
  6. 6.
    Hoi, S.C.H., Jin, R., Zhu, J., Lyu, M.R.: Semi-supervised SVM batch mode active learning with applications to image retrieval. ACM TOIS 27, 1–29 (2009)Google Scholar
  7. 7.
    Dayong Wang, Y.: Retrieval-based face annotation by weak label regularized local coordinate coding. IEEE Trans. 36 (2014)Google Scholar
  8. 8.
    Zhu, J., Hoi, S.C., Gool, L.V.: Unsupervised face alignment by robust nonrigid mapping. In: ICCV’09 (2009)Google Scholar
  9. 9.
    Wang, D., Hoi, S.C.H., He, Y.: Mining weakly labeled web facial images for search-based face annotation. In: SIGIR (2011)Google Scholar
  10. 10.
    http://www.stevenhoi.org. Accessed 28 Nov 2018
  11. 11.
    Kasar, M., Bhattacharyya, D., Kim, T.: Face recognition using neural network: a review. Int. J. Secur. Its Appl. 10(3) (2016)Google Scholar
  12. 12.
    Nandini, M., Bhargavi, P., Raja Sekhar, G.: Face recognition using neural networks. Int. J. Sci. Res. Publ. 3(3) (2013) ISSN 2250-3153Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Annasaheb Dange College of Engineering & Technology AshtaSangliIndia

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