An efficient incremental face annotation for large scale web services
- First Online:
- 112 Downloads
This paper proposes an incremental face annotation framework for sharing and publishing photographs which contain faces under a large scale web platform such as a social network service with millions of users. Unlike the conventional face recognition environment addressed by most existing works, the image databases being accessed by the large pool of users can be huge and frequently updated. A reasonable way to efficiently annotate such huge databases is to accommodate an adaptation of model parameters without the need to retrain the model all over again when new data arrives. In this work, we are particularly interested in the following issues related to increment of data: (i) the huge number of images being added at each instant, (ii) the large number of users joining the web each day, and (iii) the large number of classification systems being added at each period. We propose an efficient recursive estimation method to handle these data increment issues. Our experiments on several databases show that our proposed method achieves an almost constant execution time with comparable accuracy relative to those state-of-the-art incremental versions of principal component analysis, linear discriminant analysis and support vector machine.
KeywordsFace recognition Incremental learning Web service Recursive least squares
Unable to display preview. Download preview PDF.
- 1.Girgensohn, A., Adcock, J., & Wilcox, L. (2004). Leveraging face recognition technology to find and organize photos. In Proceedings of the 6th ACM SIGMM international workshop on multimedia information retrieval. Google Scholar
- 3.Naaman, M., Yeh, R., Garcia-Molina, H., & Paepcke, A. (2005). Leveraging context to resolve identity in photo albums. In Proceedings of the 5th ACM/IEEE-CS joint conference on digital libraries. Google Scholar
- 6.Zhang, L., Chen, L., Li, M., & Zhang, H. (2003). Automated annotation of human faces in family albums. In Proceedings of the 11th ACM international conference on multimedia (pp. 355–358). Google Scholar
- 7.Tian, Y., Liu, W., Xiao, R., Wen, F., & Tang, X. (2007). A face annotation framework with partial clustering and interactive labeling. In Proceedings of Computer Vision and Pattern Recognition (pp. 1–8). Google Scholar
- 8.Berg, T., Berg, A., Edwards, J., Maire, M., White, R., Teh, Y., Learned-Miller, E., & Forsyth, D. (2004). Names and faces in the news. In Proceedings of computer vision and pattern recognition (Vol. 2, pp. 484–854). Google Scholar
- 9.Berg, T., Berg, A., Edwards, J., & Forsyth, D. (2005). Who’s in the picture? In Proceedings of advances in neural information processing systems (pp. 137–144). Google Scholar
- 12.Yang, J., Yan, R., & Hauptmann, A. (2005). Multiple instance learning for labeling faces in broadcasting news video. In Proceedings of the 13th annual ACM international conference on multimedia (pp. 31–40). Google Scholar
- 13.Yang, J., & Hauptmann, A. (2004). Naming every individual in news video monologues. In Proceedings of the 12th annual ACM international conference on multimedia (pp. 580–587). Google Scholar
- 14.Myspace.com. Available: http://www.myspace.com.
- 15.Facebook.com. Available: http://www.facebook.com.
- 16.Flickr.com. Available: http://www.flickr.com.
- 18.Myheritage.com. Available: http://www.myheritage.com.
- 19.Riya.com. Available: http://www.riya.com.
- 20.Li, J., & Wang, J. Real-time computerized annotation of pictures. IEEE Transactions on Pattern Analysis and Machine Intelligence. Google Scholar
- 21.Like.com. Available: http://www.like.com.
- 22.Comscore. Available: http://www.comscore.com.
- 23.Becker, B., & Ortiz, E. (2008). Evaluation of face recognition techniques for applications to Facebook. In Eighth international conference on automated face and gesture recognition (AFGR). Google Scholar
- 24.Facebook—statistics (2008). Google Scholar
- 25.Stone, Z., Zickler, T., & Darrell, T. (2008). Autotagging Facebook: social network context improves photo annotation. In Workshop on Internet vision. Google Scholar
- 26.Choi, K., Byun, H., & Toh, K.-A. (2008). A collaborative face recognition framework on a social network platform. In Eighth international conference on automated face and gesture recognition (AFGR). Google Scholar
- 28.Kim, T., Wong, S., Stenger, B., Kittler, J., & Cipolla, R. (2007). Incremental linear discriminant analysis using sufficient spanning set approximations. In Proceedings of computer vision and pattern recognition. Google Scholar
- 31.Chen, W., Pan, B., Fang, B., Li, M., & Tang, J. (2008). Incremental nonnegative matrix factorization for face recognition. Mathematical Problems in Engineering. Google Scholar
- 32.Wade, W. R. (2000). An introduction to analysis (2nd ed.). New York: Prentice Hall. Google Scholar
- 33.Toh, K., Tran, Q., & Srinivasan, D. (2004). Benchmarking a reduced multivariate polynomial pattern classifier. IEEE Transactions on Pattern Analysis and Machine Intelligence, 740–755. Google Scholar
- 34.Tran, Q., Toh, K., & Srinivasan, D. (2004). Adaptation to changes in multimodal biometric authentication. In Proceedings of cybernetics and intelligent systems. Google Scholar
- 35.Wu, J., Zhou, J., & Yan, P. Incremental proximal support vector classifier for multi-class classification. In Proceedings of machine learning and cybernetics (Vol. 5). Google Scholar
- 37.Martinez, A., & Benavente, R. (1998). The AR face database (CVC Technical Report #24). Google Scholar
- 39.Liang, Z., & Li, Y. (2009). Incremental support vector machine learning in the primal and applications. Neurocomputing, 72. Google Scholar
- 40.Ozawa, S., Toh, S., Abe, S., Pang, S., & Kasabov, N. (2005). Incremental learning of feature space and classifier for face recognition. IEEE Transactions on Neural Networks, 18(5–6), 575–584. Google Scholar
- 41.AT&T. Available: http://www.uk.research.att.com/facedatabase.html.
- 44.OSUSVM. Available: http://www.ece.osu.edu/osu_svm.