Illumination Normalization-Based Face Detection under Varying Illumination

Min YAO; Hiroshi NAGAHASHI; Kota AOKI

doi:10.1587/transinf.E97.D.1590

Illumination Normalization-Based Face Detection under Varying Illumination

Min YAO, Hiroshi NAGAHASHI, Kota AOKI

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A number of well-known learning-based face detectors can achieve extraordinary performance in controlled environments. But face detection under varying illumination is still challenging. Possible solutions to this illumination problem could be creating illumination invariant features or utilizing skin color information. However, the features and skin colors are not sufficiently reliable under difficult lighting conditions. Another possible solution is to do illumination normalization (e.g., Histogram Equalization (HE)) prior to executing face detectors. However, applications of normalization to face detection have not been widely studied in the literature. This paper applies and evaluates various existing normalization methods under the framework of combining the illumination normalization and two learning-based face detectors (Haar-like face detector and LBP face detector). These methods were initially proposed for different purposes (face recognition or image quality enhancement), but some of them significantly improve the original face detectors and lead to better performance than HE according to the results of the comparative experiments on two databases. Meanwhile, we propose a new normalization method called segmentation-based half histogram stretching and truncation (SH) for face detection under varying illumination. It first employs Otsu method to segment the histogram (intensities) of the input image into several spans and then does the redistribution on the segmented spans. In this way, the non-uniform illumination can be efficiently compensated and local facial structures can be appropriately enhanced. Our method obtains good performance according to the experiments.

Publication: IEICE TRANSACTIONS on Information Vol.E97-D No.6 pp.1590-1598

Publication Date: 2014/06/01

Publicized

Online ISSN: 1745-1361

DOI: 10.1587/transinf.E97.D.1590

Type of Manuscript: PAPER

Category: Image Recognition, Computer Vision

Authors

Min YAO
  Tokyo Institute of Technology
Hiroshi NAGAHASHI
  Tokyo Institute of Technology
Kota AOKI
  Tokyo Institute of Technology

Keyword

illumination normalization, Otsu method, segmentation-based, Haar-like face detector, LBP face detector

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Min YAO, Hiroshi NAGAHASHI, Kota AOKI, "Illumination Normalization-Based Face Detection under Varying Illumination" in IEICE TRANSACTIONS on Information, vol. E97-D, no. 6, pp. 1590-1598, June 2014, doi: 10.1587/transinf.E97.D.1590.
Abstract: A number of well-known learning-based face detectors can achieve extraordinary performance in controlled environments. But face detection under varying illumination is still challenging. Possible solutions to this illumination problem could be creating illumination invariant features or utilizing skin color information. However, the features and skin colors are not sufficiently reliable under difficult lighting conditions. Another possible solution is to do illumination normalization (e.g., Histogram Equalization (HE)) prior to executing face detectors. However, applications of normalization to face detection have not been widely studied in the literature. This paper applies and evaluates various existing normalization methods under the framework of combining the illumination normalization and two learning-based face detectors (Haar-like face detector and LBP face detector). These methods were initially proposed for different purposes (face recognition or image quality enhancement), but some of them significantly improve the original face detectors and lead to better performance than HE according to the results of the comparative experiments on two databases. Meanwhile, we propose a new normalization method called segmentation-based half histogram stretching and truncation (SH) for face detection under varying illumination. It first employs Otsu method to segment the histogram (intensities) of the input image into several spans and then does the redistribution on the segmented spans. In this way, the non-uniform illumination can be efficiently compensated and local facial structures can be appropriately enhanced. Our method obtains good performance according to the experiments.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E97.D.1590/_p

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@ARTICLE{e97-d_6_1590,
author={Min YAO, Hiroshi NAGAHASHI, Kota AOKI, },
journal={IEICE TRANSACTIONS on Information},
title={Illumination Normalization-Based Face Detection under Varying Illumination},
year={2014},
volume={E97-D},
number={6},
pages={1590-1598},
abstract={A number of well-known learning-based face detectors can achieve extraordinary performance in controlled environments. But face detection under varying illumination is still challenging. Possible solutions to this illumination problem could be creating illumination invariant features or utilizing skin color information. However, the features and skin colors are not sufficiently reliable under difficult lighting conditions. Another possible solution is to do illumination normalization (e.g., Histogram Equalization (HE)) prior to executing face detectors. However, applications of normalization to face detection have not been widely studied in the literature. This paper applies and evaluates various existing normalization methods under the framework of combining the illumination normalization and two learning-based face detectors (Haar-like face detector and LBP face detector). These methods were initially proposed for different purposes (face recognition or image quality enhancement), but some of them significantly improve the original face detectors and lead to better performance than HE according to the results of the comparative experiments on two databases. Meanwhile, we propose a new normalization method called segmentation-based half histogram stretching and truncation (SH) for face detection under varying illumination. It first employs Otsu method to segment the histogram (intensities) of the input image into several spans and then does the redistribution on the segmented spans. In this way, the non-uniform illumination can be efficiently compensated and local facial structures can be appropriately enhanced. Our method obtains good performance according to the experiments.},
keywords={},
doi={10.1587/transinf.E97.D.1590},
ISSN={1745-1361},
month={June},}

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TY - JOUR
TI - Illumination Normalization-Based Face Detection under Varying Illumination
T2 - IEICE TRANSACTIONS on Information
SP - 1590
EP - 1598
AU - Min YAO
AU - Hiroshi NAGAHASHI
AU - Kota AOKI
PY - 2014
DO - 10.1587/transinf.E97.D.1590
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E97-D
IS - 6
JA - IEICE TRANSACTIONS on Information
Y1 - June 2014
AB - A number of well-known learning-based face detectors can achieve extraordinary performance in controlled environments. But face detection under varying illumination is still challenging. Possible solutions to this illumination problem could be creating illumination invariant features or utilizing skin color information. However, the features and skin colors are not sufficiently reliable under difficult lighting conditions. Another possible solution is to do illumination normalization (e.g., Histogram Equalization (HE)) prior to executing face detectors. However, applications of normalization to face detection have not been widely studied in the literature. This paper applies and evaluates various existing normalization methods under the framework of combining the illumination normalization and two learning-based face detectors (Haar-like face detector and LBP face detector). These methods were initially proposed for different purposes (face recognition or image quality enhancement), but some of them significantly improve the original face detectors and lead to better performance than HE according to the results of the comparative experiments on two databases. Meanwhile, we propose a new normalization method called segmentation-based half histogram stretching and truncation (SH) for face detection under varying illumination. It first employs Otsu method to segment the histogram (intensities) of the input image into several spans and then does the redistribution on the segmented spans. In this way, the non-uniform illumination can be efficiently compensated and local facial structures can be appropriately enhanced. Our method obtains good performance according to the experiments.
ER -