| Full text | |
| Author(s): |
Scheirer, Walter J.
[1]
;
Rocha, Anderson de Rezende
[2]
;
Parris, Jonathan
[1]
;
Boult, Terrance E.
[1]
Total Authors: 4
|
| Affiliation: | [1] Univ Colorado, Dept Comp Sci, Colorado Springs, CO 80907 - USA
[2] Univ Estadual Campinas, Inst Comp, BR-13083852 Sao Paulo - Brazil
Total Affiliations: 2
|
| Document type: | Journal article |
| Source: | IEEE Transactions on Information Forensics and Security; v. 7, n. 4, p. 1214-1224, AUG 2012. |
| Web of Science Citations: | 6 |
| Abstract | |
In this paper, we consider meta-recognition, an approach for postrecognition score analysis, whereby a prediction of matching accuracy is made from an examination of the tail of the scores produced by a recognition algorithm. This is a general approach that can be applied to any recognition algorithm producing distance or similarity scores. In practice, meta-recognition can be implemented in two different ways: a statistical fitting algorithm based on the extreme value theory, and a machine learning algorithm utilizing features computed from the raw scores. While the statistical algorithm establishes a strong theoretical basis for meta-recognition, the machine learning algorithm is more accurate in its predictions in all of our assessments. In this paper, we present a study of the machine learning algorithm and its associated features for the purpose of building a highly accurate meta-recognition system for security and surveillance applications. Through the use of feature-and decision-level fusion, we achieve levels of accuracy well beyond those of the statistical algorithm, as well as the popular ``cohort{''} model for postrecognition score analysis. In addition, we also explore the theoretical question of why machine learning-based algorithms tend to outperform statistical meta-recognition and provide a partial explanation. We show that our proposed methods are effective for a variety of different recognition applications across security and forensics-oriented computer vision, including biometrics, object recognition, and content-based image retrieval. (AU) | |
| FAPESP's process: | 10/05647-4 - Digital forensics: collection, organization, classification and analysis of digital evidences |
| Grantee: | Anderson de Rezende Rocha |
| Support Opportunities: | Research Grants - Young Investigators Grants |