ABSTRACT
Automatic annotation of photographs is one of the most desirable needs in family photograph management systems. In this paper, we present a learning framework to automate the face annotation in family photograph albums. Firstly, methodologies of content-based image retrieval and face recognition are seamlessly integrated to achieve automated annotation. Secondly, face annotation is formulated in a Bayesian framework, in which the face similarity measure is defined as maximum a posteriori (MAP) estimation. Thirdly, to deal with the missing features, marginal probability is used so that samples which have missing features are compared with those having the full feature set to ensure a non-biased decision. The experimental evaluation has been conducted within a family album of few thousands of photographs and the results show that the proposed approach is effective and efficient in automated face annotation in family albums.
- Chen L., Hu B., Zhang L., Li M. and Zhang H.J., "Face annotation for family photo album management", International Journal of Image and Graphics, p.1-14, Vol. 3, No. 1, 2003.Google Scholar
- Huang J., Kumar S. R., Mitra M., Zhu W. J. and Zabih R., "Image indexing using color correlograms", In IEEE Conf. on Computer Vision and Pattern Recognition, p. 762, 1997. Google ScholarDigital Library
- Moghaddam, B., Jebara, T. and Pentland, A., "Bayesian Face Recognition", Pattern Recognition, Vol 33, Issue 11, p.1771--1782, 2000Google ScholarCross Ref
- Phillips P., Moon H., Rizvi S. and Rauss P., "The FERET evaluation methodology for face-recognition algorithms", IEEE Trans. PAMI, vol.22, p.1090--1103, 2000 Google ScholarDigital Library
- Platt J. "Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods". In Advances in Large Margin Classifiers. MIT Press, 1999.Google Scholar
- Xiao R., Li M.J., Zhang H.J., "Robust Multi-Pose Face Detection in Images", to be appeared in IEEE Trans. on CSVT Special Issue on Biometrics, 2003 Google ScholarDigital Library
- Yan S.C., Liu C., Li S.Z., et al., "Texture-Constrained Active Shape Models", In Proc. International Workshop on Generative-Model-Based Vision, Denmark. May, 2002.Google Scholar
- Yang M. H., Kriegman D. and Ahuja N., "Detecting Faces in Images: A Survey", IEEE Trans. PAMI, p. 34, 24(1),2002 Google ScholarDigital Library
- Yu H., Li M., Zhang H. and Feng J., "Color texture moment for content-based image retrieval", Proc. IEEE Intl Conf. on Image Processing, September, 2002Google ScholarCross Ref
- Zhao W., Chellappa R., Rosenfeld A. and Phillips P., "Face recognition: A literature survey", Technical Report, Maryland University, CfAR CAR-TR-948, 2000Google Scholar
Index Terms
- Automated annotation of human faces in family albums
Recommendations
Efficient propagation for face annotation in family albums
MULTIMEDIA '04: Proceedings of the 12th annual ACM international conference on MultimediaIn this paper, we propose and investigate a new user scenario for face annotation, in which users are allowed to multi-select a group of otogras and assign names to these otogras. The system will then attempt to propagate names from otogra level to face ...
Face annotation for personal photos using context-assisted face recognition
MIR '08: Proceedings of the 1st ACM international conference on Multimedia information retrievalFace annotation for personal photos has a number of potential applications in Multimedia Information Retrieval (MIR). We propose a novel face annotation method that systematically combines contextual information of photos with traditional Face ...
Robust Statistical Frontalization of Human and Animal Faces
The unconstrained acquisition of facial data in real-world conditions may result in face images with significant pose variations, illumination changes, and occlusions, affecting the performance of facial landmark localization and recognition methods. In ...
Comments