Face Indexing and Retrieval by Spatial Similarity

In this paper a face indexing scheme based on spatial similarity is proposed. Spatial scattering of anatomically relevant dominant points on faces is preserved in the kd-tree index structure for efficient retrieval. The methodology is invariant to linear transformation and is robust for pose and expression variations. Experimentation on ORL face database has corroborated the retrieval effectiveness and time efficiency of the proposed methodology.

[1]  Hiroshi Yano,et al.  A new retrieval system for a database of 3D facial images. , 2005, Forensic science international.

[2]  Geeng-Neng You,et al.  A new knowledge-based face image indexing system through the Internet , 2006, 2006 8th International Conference Advanced Communication Technology.

[3]  C Sforza,et al.  Sexual dimorphism in the human face assessed by euclidean distance matrix analysis. , 1993, Journal of anatomy.

[4]  Essam A. El-Kwae,et al.  Efficient content-based indexing of large image databases , 2000, TOIS.

[5]  Alice J. O'Toole,et al.  Low-dimensional representation of faces in higher dimensions of the face space , 1993 .

[6]  Chengjun Liu,et al.  Comparative assessment of content-based face image retrieval in different color spaces , 2005, Int. J. Pattern Recognit. Artif. Intell..

[7]  Ashok Samal,et al.  How effective are landmarks and their geometry for face recognition? , 2006, Comput. Vis. Image Underst..

[8]  Suphakant Phimoltares,et al.  Face detection and facial feature localization without considering the appearance of image context , 2007, Image Vis. Comput..

[9]  Roberto Brunelli,et al.  Face Recognition: Features Versus Templates , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  L. D. Harmon,et al.  Identification of human faces , 1971 .

[11]  Venkat N. Gudivada.  ThetaR-String: A Geometry-Based Representation for Efficient and Effective Retrieval of Images by Spatial Similarity , 1998, IEEE Trans. Knowl. Data Eng..

[12]  Michael W. Spratling.  Learning Image Components for Object Recognition , 2006, J. Mach. Learn. Res..

[13]  Andrea F. Abate,et al.  One to Many 3D Face Recognition Enhanced Through k-d-Tree Based Spatial Access , 2005, Multimedia Information Systems.

[14]  David J. Kriegman,et al.  Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection , 1996, ECCV.

[15]  William I. Grosky,et al.  Indexing and retrieval of images by spatial constraints , 2003, J. Vis. Commun. Image Represent..

[16]  Xian Zhang,et al.  Video Program Clustering Indexing Based on Face Recognition Hybrid Model of Hidden Markov Model and Support Vector Machine , 2004, IWCIA.

[17]  Sethuraman Panchanathan,et al.  Using Genetic Algorithms to Find Person-Specific Gabor Feature Detectors for Face Indexing and Recognition , 2006, ICB.

[18]  Nicholas Costen,et al.  How Should We RepresentFaces for Automatic Recognition? , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  Francesco M. Donini,et al.  Retrieval by spatial similarity: an algorithm and a comparative evaluation , 2004, Pattern Recognit. Lett..

[20]  Hiroyasu Koshimizu,et al.  Keyword and face image retrieval based on latent semantic indexing , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

[21]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.

[22]  Yutao Qi,et al.  Robust visual similarity retrieval in single model face databases , 2005, Pattern Recognit..

[23]  Yuchun Fang,et al.  Experiments in Mental Face Retrieval , 2005, AVBPA.

[24]  Jeffrey Scott Vitter,et al.  Bkd-Tree: A Dznamic Scalable kd-Tree , 2003, SSTD.