Linear regression for head pose analysis

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Abstract

Extensive research has been conducted to estimate and analyze head poses for various applications. Most existing methods tend to detect facial features and locate landmarks on a face for pose estimation. However, the sensitivity to occlusion of some face parts with key features and uncontrolled illumination of face images make the facial feature detection vulnerable. In this paper, we propose a framework for pose estimation without the need of face features or landmarks detection. Specifically, we formulate the pose estimation as a linear regression applied to the pose space. This method is based on the assumption that pose space cannot be linearly approximated in the pose subspace. The experimental results strongly support this assumption. In cases where the database does not obtain various poses in the intraclass, we propose to generate those poses through a 3D reconstruction and projection method. The experiment
conducted on the CMU MultiPIE and IMM Face database has shown the effectiveness of the proposed method.
Original languageEnglish
Title of host publicationProceedings of the 2014 international joint conference on neural networks (IJCNN 2014)
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages987-992
ISBN (Electronic)2161-4393 (ISSN)
ISBN (Print)9781479914821
DOIs
Publication statusPublished - Nov 2014
EventIEEE 2014 International Joint Conference on Neural Networks - Beijing, China
Duration: 6 Jul 201411 Jul 2014

Conference

ConferenceIEEE 2014 International Joint Conference on Neural Networks
Country/TerritoryChina
CityBeijing
Period6/07/1411/07/14

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