Conformal Prediction for Automatic Face Recognition

Charalambos Eliades, Harris Papadopoulos
Proceedings of the Sixth Workshop on Conformal and Probabilistic Prediction and Applications, PMLR 60:62-81, 2017.

Abstract

Automatic Face Recognition (AFR) has been the subject of many research studies in the past two decades and has a wide range of applications. The provision of some kind of indication of the likelihood of a recognition being correct is a desirable property of AFR techniques in many applications, such as for the detection of wanted persons or for performing post-processing in automatic annotation of photographs. This paper investigates the use of the Conformal Prediction (CP) framework for providing reliable confidence information for AFR. In particular we combine CP with two classifiers based on calculating similarities between images using Scale Invariant Feature Transformation (SIFT) features. We examine and compare the performance of several nonconformity measures for the particular task in terms of their accuracy and informational eficiency.

Cite this Paper

BibTeX
@InProceedings{pmlr-v60-eliades17a, title = {Conformal Prediction for Automatic Face Recognition}, author = {Eliades, Charalambos and Papadopoulos, Harris}, booktitle = {Proceedings of the Sixth Workshop on Conformal and Probabilistic Prediction and Applications}, pages = {62--81}, year = {2017}, editor = {Gammerman, Alex and Vovk, Vladimir and Luo, Zhiyuan and Papadopoulos, Harris}, volume = {60}, series = {Proceedings of Machine Learning Research}, month = {13--16 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v60/eliades17a/eliades17a.pdf}, url = {https://proceedings.mlr.press/v60/eliades17a.html}, abstract = {Automatic Face Recognition (AFR) has been the subject of many research studies in the past two decades and has a wide range of applications. The provision of some kind of indication of the likelihood of a recognition being correct is a desirable property of AFR techniques in many applications, such as for the detection of wanted persons or for performing post-processing in automatic annotation of photographs. This paper investigates the use of the Conformal Prediction (CP) framework for providing reliable confidence information for AFR. In particular we combine CP with two classifiers based on calculating similarities between images using Scale Invariant Feature Transformation (SIFT) features. We examine and compare the performance of several nonconformity measures for the particular task in terms of their accuracy and informational eficiency.} }
Endnote
%0 Conference Paper %T Conformal Prediction for Automatic Face Recognition %A Charalambos Eliades %A Harris Papadopoulos %B Proceedings of the Sixth Workshop on Conformal and Probabilistic Prediction and Applications %C Proceedings of Machine Learning Research %D 2017 %E Alex Gammerman %E Vladimir Vovk %E Zhiyuan Luo %E Harris Papadopoulos %F pmlr-v60-eliades17a %I PMLR %P 62--81 %U https://proceedings.mlr.press/v60/eliades17a.html %V 60 %X Automatic Face Recognition (AFR) has been the subject of many research studies in the past two decades and has a wide range of applications. The provision of some kind of indication of the likelihood of a recognition being correct is a desirable property of AFR techniques in many applications, such as for the detection of wanted persons or for performing post-processing in automatic annotation of photographs. This paper investigates the use of the Conformal Prediction (CP) framework for providing reliable confidence information for AFR. In particular we combine CP with two classifiers based on calculating similarities between images using Scale Invariant Feature Transformation (SIFT) features. We examine and compare the performance of several nonconformity measures for the particular task in terms of their accuracy and informational eficiency.
APA
Eliades, C. & Papadopoulos, H.. (2017). Conformal Prediction for Automatic Face Recognition. Proceedings of the Sixth Workshop on Conformal and Probabilistic Prediction and Applications, in Proceedings of Machine Learning Research 60:62-81 Available from https://proceedings.mlr.press/v60/eliades17a.html.

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