Detecting, localizing and classifying visual traits from arbitrary viewpoints using probabilistic local feature modeling

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4778 LNCS, Issue , 2007, Pages 154-167

  Toews, Matthew ^a   Arbel, Tal ^a  

^a MCGILL UNIVERSITY   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

DATABASE SYSTEMS; FEATURE EXTRACTION; MATHEMATICAL MODELS;

BAYESIAN CLASSIFIER; TRAIT CLASSIFICATION;

FACE RECOGNITION;

EID: 38149012177     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-75690-3_12     Document Type: Conference Paper

References (25)

1. Feret face database, http://www.itl.nist.gov/iad/humanid/colorferet
2. Baluja, S., Rowley, H.A.: Boosting sex identification performance. IJCV 71(1), 111-119 (2007)
3. Carneiro, G., Jepson, A.: Multi-scale phase-based local features. In: CVPR, vol. 1. pp. 736-743 (2003)
4. Carneiro, G., Lowe, D.G.: Sparse flexible models of local features, In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3953, pp. 29-43. Springer, Heidelberg (2006)
5. CMU Face Group. Frontal and profile face databases, http://vase.ri.emu. edu/idb/html/face/
6. Crandal, D., Felzenswalb, P., Huttenlocher, D.: Spatial priors for part-based recognition using statistical models. In: CVPR, vol. 1, pp. 10-17 (2005)
7. Dorko, G., Schmid, C.: Selection of scale-invariant parts for object class recognition. In: ICCV, pp. 634-640 (2003)
8. Fergus, R., Perona, P., Zisserman, A.: Weakly supervised scale-invariant learning of models for visual recognition. IJCV 71(3), 273-303 (2006)
9. Gutta, S., Wechsler, H., Phillips, P.: Gender and ethnic classification of human faces using hybrid classifiers. In: FGR, pp. 194-199 (1998)
10. Jain, A., Huang, J., Fang, S.: Gender identification using frontal facial images. In: ICME (2005)
11. Jaynes, E.: Prior probabilities. IEEE Transactions of systems, science, and cybernetics 4(3), 227-241 (1968)
12. Jordan, M.I.: An Introduction to Probabilistic Graphical Models (2003) (in preparation)
13. Kadir, T., Brady, M.: Saliency, scale and image description. IJCV 45(2), 83-105 (2001)
14. Kim, H.-C., Kim, D., Ghahramani, Z., Bang, S.Y.: Appearance-based gender classification with gaussian processes. PRL 27, 618-626 (2006)
15. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. IJCV 60(2), 91-110 (2004)
16. Mikolajczyk, K., Schmid, C.: Indexing based on scale invariant interest points. In: ICCV, pp. I: 525-531 (2001)
17. Moghaddam, B., Yang, M.: Learning gender with support faces. PAMI 24(5), 707-711 (2002)
18. Shakhnarovich, G., Viola, P.A., Moghaddam, B.: A unified learning framework for real time face detection and classification. In: FGR (2002)
19. Sivic, J., Russell, B.C., Efros, A.A., Zisserman, A., Freeman, W.T.: Discovering objects and their localization in images. In: ICCV, pp. 370-377 (2005)
20. Thomas, A., Ferrari, V., Leibe, B., Tuytelaars, T., Schiele, B., Van Gool, L.: Towards multiview object class detection. In: CVPR (2006)
21. Toews, M., Arbel, T.: Detection over viewpoint via the object class invariant. In: ICPR, pp. 765-768 (2006)
22. Turk, M., Pentland, A.P.: Eigenfaces for recognition. CogNeuro 3(1), 71-96 (1991)
23. Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: CVPR, vol. 1, pp. 511-518 (2001)
24. Weber, M., Welling, M., Perona, P.: Unsupervised learning of models for recognition. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1842, pp. 18-32. Springer, Heidelberg (2000)
25. Yang, Z., Li, M., Ai, H.: An experimental study on automatic face gender classification. In: ICPR, pp. 1099-1102 (2006)