Automatic Photo Indexing Based on Person Identity

In this paper, we propose a novel approach to automatically index digital home photos based on person identity. A person is identified by his/her face and clothes. The proposed method consists of two parts: clustering and indexing. In the clustering, a series of unlabeled photos is aligned in taken-time order, and is divided into several sub-groups by situation. The situation groups are decided by time and visual differences. In the indexing, SVMs are trained with features of pre-indexed faces to model target persons. The representative feature vector of the person group from the clustering is queried to the trained SVMs. Each SVM outputs a numeric confidence value about the query person group. The query person group is determined to the target person by the most confident SVM. The experimental results showed that the proposed method outperformed traditional person indexing method using only face feature and its performance increased to 93.56% from 72.31%.

[1]  Mingjing Li,et al.  Automated annotation of human faces in family albums , 2003, MULTIMEDIA '03.

[2]  Yong Man Ro,et al.  Automated situation clustering of home photos for digital albuming , 2005, IS&T/SPIE Electronic Imaging.

[3]  Azriel Rosenfeld,et al.  Face recognition: A literature survey , 2003, CSUR.

[4]  Bo Wu,et al.  Fast rotation invariant multi-view face detection based on real Adaboost , 2004, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings..

[5]  David G. Stork,et al.  Pattern Classification (2nd ed.) , 1999 .

[6]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[7]  Narendra Ahuja,et al.  Detecting Faces in Images: A Survey , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  David G. Stork,et al.  Pattern Classification , 1973 .

[9]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[10]  B. S. Manjunath,et al.  Introduction to mpeg-7 , 2002 .

[11]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[12]  John Moody,et al.  Fast Learning in Networks of Locally-Tuned Processing Units , 1989, Neural Computation.

[13]  Keiji Kojima,et al.  The Digital Album: a personal file-tainment system , 1996, Proceedings of the Third IEEE International Conference on Multimedia Computing and Systems.

[14]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory, Second Edition , 2000, Statistics for Engineering and Information Science.

[15]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.