Marking Celebrity Faces Utilizing Annotation by Mining Weakly Labeled Facial Images

Ms. Jyoti H. Jadhav, Mr. Pankaj M. Agarkar


Face annotation is a note or description added to the image for better understanding. It can help to improve better search due to detailed description. Face annotation in images and video enjoys many potential applications in multimedia information retrieval. Face annotation usually requires many training data labeled by hand in order to build effective classifiers. This is particularly challenging when annotating faces on large-scale collections of media data, in which huge labeling efforts would be very expensive. The goal of the system is to annotate the celebrity face images from childhood to recent face image and also to annotate the unseen faces in video. In particular, given a facial image, we first retrieve top n similar instances from facial image database. Initially the database contains images and description mapping of that image. Later the celebrity face image and/or the video that need to be processed will be considered. The detected face image will be processed with existing database. The matching result will produce mating annotation or null. For the video; that video will be converted to frames. This frame will act as an image. Further training can be same as for the celebrity face image. A know framework search based face annotation (SBFA) is there to mine weakly labeled facial images is used. Use of annotation will help for user to search desire image and video e.g. news videos. Automatic tagging of people will improve the search results. Also if system gets implemented in social network then it will overcome the drawback of current existing system which tags manually like Facebook.

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J.Y. Choi, W.D. Neve, K.N. Plataniotis, and Y.M. Ro, Collaborative

Face Recognition for Improved Face Annotation in Personal Photo

Collections Shared on Online Social Networks, IEEE Trans.

Multimedia, vol. 13, no. 1, pp. 14-28, Feb. 2011.

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.

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.

L. Wu, S.C.H. Hoi, R. Jin, J. Zhu, and N. Yu, Distance Metric

Learning from Uncertain Side Information for Automated Photo

Tagging, ACM Trans. Intelligent Systems and Technology, vol. 2,

no. 2, p. 13, 2011.

P. Wu, S.C.H. Hoi, P. Zhao, and Y. He, Mining Social Images with

Distance Metric Learning for Automated Image Tagging, Proc.

Fourth ACM Intl Conf. Web Search and Data Mining (WSDM

, pp. 197-206, 2011.

Dayong Wang, Steven C. H. Hoi, Ying He, and Jianke Zhu,

Mining Weakly Labeled Web Facial Images for Search-Based Face

Annotation, IEEE Trans. on Knowledge and Data Engineering,

vol. 26, no. 1, Jan 2014.

Dayong Wang, Steven C.H. Hoi, Ying He, Jianke Zhu, Tao Mei,

and Jiebo Luo, Retrieval-Based Face Annotation by Weak Label

Regularized Local Coordinate Coding, IEEE Trans. on PATTERN


March 2014.

M. Tapaswi, M. Bauml and R. Stiefelhagen, ”Knock! Knock!

Who is it?” Probabilistic person identification in TV-series,” in

Proc. IEEE Comput. Vis. Pattern Recog., Jun. 2012, pp. 2658-2665.

S. Satoh, Y. Nakamura, and T. Kanade, ”Name-It: Naming and

detecting faces in news videos,” IEEE Multimedia, vol. 6, no. 1,

pp. 22-35, Jan-Mar. 1999.

M. Guillaumin, T. Mensink, J. Verbeek, and C. Schmid,

Automatic Face Naming with Caption-Based Supervision, Proc.

IEEE Conf. Computer Vision and Pattern Recognition (CVPR),

Emma Taborsky,Kristen Allen,Austin Blanton, Anil K

Jain,Brendan F Klare(2015), Annotating Unconstrained Face

Imagery : A Scalable Approach , The 8th ICB, Phuket, Thailand,

May 19-22, 2015.


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