Friend recommendation according to appearances on photos

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Friend recommendation according to appearances on photos

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International Multimedia Conference archive
Proceedings of the seventeen ACM international conference on Multimedia table of contents

Beijing, China

DEMONSTRATION SESSION: Technical demonstrations session 1 table of contents

Pages 987-988

Year of Publication: 2009

ISBN:978-1-60558-608-3

Authors

Zhipeng Wu	Graduate University of Chinese Academy of Sciences, Beijing, China
Shuqiang Jiang	Key Lab of Intell. Info. Process., Inst. of Comput. Tech., Chinese Academy of Sciences, Beijing, China
Qingming Huang	Graduate University of Chinese Academy of Sciences, Beijing, China

Sponsor

SIGMULTIMEDIA: ACM Special Interest Group on Multimedia

Publisher

ACM New York, NY, USA

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ABSTRACT

Unlike the questionnaire based friend recommendation scheme used in Social Network Service (SNS) websites nowadays (e.g. online dating sites, online matchmaking sites), we focus on the fact that most of the online users may be interested in the strangers whose appearances are somehow attractive according to their own preferences. In this paper, we present a friend recommendation system based on the appearances on photos. The system is built upon 5000 portraits photos as source dataset with another 50 photos as training set. Once the user provides rating to several photos in the training set, we first build his/her appearance prefe-rence model based on face detection and multi-features cooperation. Then, the images in the source are ranked according to different features respectively. Finally, the results of multi-features are fused via the method of Borda count. The system is a useful complement to the conventional psychological tests based friend rec-ommendation scheme. It is easy to play with and of a lot of fun.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	iResearch, http://www.iresearch.com/.
	2	Borda Count. http://en.wikipedia.org/wiki/Borda_count.
	3	R. Lienhart and J. Maydt, "An extended set of Haar-like features for rapid object detection," in Proc. IEEE Conference on Image Processing, vol. 1, pp. 900--903, 2002.
	4	B.S. Manjunath and W.Y. Ma, "Texture features for browsing and retrieval of image data," IEEE Trans. Pattern Anal. Mach. Intell., 18(8), pp. 837--842, 1996.