Modeling and predicting face recognition system performance based on analysis of similarity scores.

Full Abstract

This paper presents methods of modeling and predicting face recognition (FR) system performance based on analysis of similarity scores. We define the performance of an FR system as its recognition accuracy, and consider the intrinsic and extrinsic factors affecting its performance. The intrinsic factors of an FR system include the gallery images, the FR algorithm, and the tuning parameters. The extrinsic factors include mainly query image conditions. For performance modeling, we propose the concept of "perfect recognition," based on which a performance metric is extracted from perfect recognition similarity scores (PRSS) to relate the performance of an FR system to its intrinsic factors. The PRSS performance metric allows tuning FR algorithm parameters offline for near optimal performance. In addition, the performance metric extracted from query images is used to adjust face alignment parameters online for improved performance. For online prediction of the performance of an FR system on query images, features are extracted from the actual recognition similarity scores and their corresponding PRSS. Using such features, we can predict online if an individual query image can be correctly matched by the FR system, based on which we can reduce the incorrect match rates. Experimental results demonstrate that the performance of an FR system can be significantly improved using the presented methods.

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Author information

Author/s: Wang, Peng (P); Ji, Qiang (Q); Wayman, James L (JL);

Affiliation: Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA. wpeng(-atsign-)ieee.org

Journal and publication information

Publication Type: Evaluation Studies; Journal Article; Research Support, Non-U.S. Gov't

Journal: IEEE transactions on pattern analysis and machine intelligence (IEEE Trans Pattern Anal Mach Intell), published in United States. (Language: eng)

Reference: 2007-Apr; vol 29 (issue 4) : pp 665-70

Dates: Created 2007/02/14; Completed 2007/04/24;

PMID: 17299223, status: MEDLINE (last retrieval date: 12/26/2008)

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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MeSH headings (categories)

This article was linked to the MESH Headings shown below.

Algorithms
Artificial Intelligence
Biometry - methods
Computer Simulation
Face - anatomy & histology
Humans

Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Models, Biological
Pattern Recognition, Automated - methods
Reproducibility of Results
Sensitivity and Specificity

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