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A comparison of features for regression-based driver head pose estimation under varying illumination conditions.

Walger, D.J. and Breckon, T.P. and Gaszczak, A. and Popham, T. (2014) 'A comparison of features for regression-based driver head pose estimation under varying illumination conditions.', in Computational Intelligence for Multimedia Understanding (IWCIM), 2014 International Workshop on, 1-2 November 2014, Paris, France ; proceedings. , pp. 84-89.


Head pose estimation provides key information about driver activity and awareness. Prior comparative studies are limited to temporally consistent illumination conditions under the assumption of brightness constancy. By contrast the illumination conditions inside a moving vehicle vary considerably with environmental conditions. In this study we present a base comparison of three features for head pose estimation, via support vector machine regression, based on Histogram of Oriented Gradient (HOG) features, Gabor filter responses and Active Shape Model (ASM) landmark features. These, reputedly illumination invariant, are presented through a common face localization framework from which we estimate driver head pose in two degrees-of-freedom and compare against a baseline approach for recovering head pose via weak perspective geometry. Evaluation is performed over a number of invehicle sequences, exhibiting uncontrolled illumination variation, in addition to ground truth data-sets, with controlled illumination changes, upon which we achieve a minimal ∼12° and ∼15° mean error in pitch and yaw respectively via ASM landmark features.

Item Type:Book chapter
Keywords:Head pose, Driver head tracking, Gaze tracking, Pose estimation regression.
Full text:(AM) Accepted Manuscript
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Publisher statement:© 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Date accepted:No date available
Date deposited:04 February 2015
Date of first online publication:November 2014
Date first made open access:No date available

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