Which parts of the face give out your identity?

Omar Ocegueda; Shishir K. Shah; Ioannis A. Kakadiaris

doi:10.1109/CVPR.2011.5995613

Which parts of the face give out your identity?

Omar Ocegueda, Shishir K. Shah, Ioannis A. Kakadiaris

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

37 Scopus citations

Abstract

We present a Markov Random Field model for the analysis of lattices (e.g., images or 3D meshes) in terms of the discriminative information of their vertices. The proposed method provides a measure field that estimates the probability of each vertex to be discriminative or non-discriminative. As an application of the proposed framework, we present a method for the selection of compact and robust features for 3D face recognition. The resulting signature consists of 360 coefficients, based on which we are able to build a classifier yielding better recognition rates than currently reported in the literature. The main contribution of this work lies in the development of a novel framework for feature selection in scenarios in which the most discriminative information is known to be concentrated along piece-wise smooth regions of a lattice.

Original language	English (US)
Title of host publication	2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	641-648
Number of pages	8
ISBN (Print)	9781457703942
DOIs	https://doi.org/10.1109/CVPR.2011.5995613
State	Published - 2011

Publication series

Name	Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN (Print)	1063-6919

ASJC Scopus subject areas

Software
Computer Vision and Pattern Recognition

Access to Document

10.1109/CVPR.2011.5995613

Cite this

Ocegueda, O., Shah, S. K., & Kakadiaris, I. A. (2011). Which parts of the face give out your identity? In 2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011 (pp. 641-648). [5995613] (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CVPR.2011.5995613

Which parts of the face give out your identity? / Ocegueda, Omar; Shah, Shishir K.; Kakadiaris, Ioannis A.

2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011. Institute of Electrical and Electronics Engineers Inc., 2011. p. 641-648 5995613 (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Ocegueda, O, Shah, SK & Kakadiaris, IA 2011, Which parts of the face give out your identity? in 2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011., 5995613, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Institute of Electrical and Electronics Engineers Inc., pp. 641-648. https://doi.org/10.1109/CVPR.2011.5995613

Ocegueda O, Shah SK, Kakadiaris IA. Which parts of the face give out your identity? In 2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011. Institute of Electrical and Electronics Engineers Inc. 2011. p. 641-648. 5995613. (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition). https://doi.org/10.1109/CVPR.2011.5995613

@inproceedings{bbda18c9073e4bbebbb0ee48a4dcc264,

title = "Which parts of the face give out your identity?",

abstract = "We present a Markov Random Field model for the analysis of lattices (e.g., images or 3D meshes) in terms of the discriminative information of their vertices. The proposed method provides a measure field that estimates the probability of each vertex to be discriminative or non-discriminative. As an application of the proposed framework, we present a method for the selection of compact and robust features for 3D face recognition. The resulting signature consists of 360 coefficients, based on which we are able to build a classifier yielding better recognition rates than currently reported in the literature. The main contribution of this work lies in the development of a novel framework for feature selection in scenarios in which the most discriminative information is known to be concentrated along piece-wise smooth regions of a lattice.",

author = "Omar Ocegueda and Shah, {Shishir K.} and Kakadiaris, {Ioannis A.}",

year = "2011",

doi = "10.1109/CVPR.2011.5995613",

language = "English (US)",

isbn = "9781457703942",

series = "Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "641--648",

booktitle = "2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011",