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Self-dependent 3D face rotational alignment using the nose region

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2 Author(s)
M. Emambakhsh ; Department of Electronic and Electrical Engineering, University of Bath, BA2 7AY, UK ; A. N. Evans

One of the challenging issues for 3D face recognition is face alignment. Many alignment algorithms are computationally expensive, making them unsuitable for real-time biometrics, or not robust enough to detect large variations in pose. In this work, a novel algorithm for 3D face rotational alignment is proposed, that uses the nose region. After preprocessing and nose region identification, alignment is performed by applying two energy functions to the nose footprint, identified as the largest filled region in the inverted depth map. These functions are minimised using Simulated Annealing and the Levenberg-Marqurdt algorithm. The energy minimisation and segmentation procedures continue iteratively until a stopping criterion is met. The method has been applied to images from the Face Recognition Grand Challenge (FRGC) v2 dataset and the consistency of its alignment has been verified using the iterative closest point (ICP) algorithm. As a self-dependent algorithm, it does not require a pre-aligned image as a reference and also has a high computational speed, approximately three times faster than the brute force ICP technique.

Published in:

Imaging for Crime Detection and Prevention 2011 (ICDP 2011), 4th International Conference on

Date of Conference:

3-4 Nov. 2011