An illumination normalization model for face recognition under varied lighting conditions

An, Gaoyun; Wu, Jiying; Ruan, Qiuqi

doi:10.1016/j.patrec.2010.01.021

Cited by 34 publications

(13 citation statements)

References 25 publications

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“…In [42], face images are roughly partitioned into four geometrically regular parts for illumination invariant face recognition. Yet regular regions are somewhat arbitrary and difficult to conquer the non-uniform illumination problem.…”

Section: Segmentation-based Half Histogram Truncation and Stretching mentioning

confidence: 99%

Illumination Normalization-Based Face Detection under Varying Illumination

Yao

Nagahashi

Aoki

2014

IEICE Trans. Inf. & Syst.

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SUMMARYA 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.

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Section: Segmentation-based Half Histogram Truncation and Stretching mentioning

confidence: 99%

Illumination Normalization-Based Face Detection under Varying Illumination

Yao

Nagahashi

Aoki

2014

IEICE Trans. Inf. & Syst.

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“…However, optimal choice of the parameter gamma is image dependent. In [2], [3], images are partitioned into several regions, and HE and/or gamma correction are applied to each region. A disadvantage of this approach is that transitions of colors and intensities are not smooth at the boundaries between different regions.…”

Section: A Previous Workmentioning

confidence: 99%

“…In [8], an iterative adaptive smoothing method is used to estimate L, where the weights of pixels are determined as functions of gradients and inhomogeneities. To estimate L, [2], [9] suggest minimizing an objective function based on the square of the first order differential and the difference of the illumination L from the given image. The estimated illumination component is further enhanced in [9] using gamma correction.…”

Section: A Previous Workmentioning

confidence: 99%

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Illumination Normalization Using Weighted Gradient Integral Images

Liu¹,

Dominicis²,

Martin³

et al. 2013

LNSE

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Abstract-Captured images arise through interaction between objects of interest and illuminating light sources. If the latter are unevenly distributed, or are too strong or too weak, the image can have low contrast either locally or globally, impeding its interpretation and reducing its usefulness. In practice, control of illumination conditions is challenging, and not always possible. Thus, we propose a novel method to post-process captured images to reduce the effects of the illumination. We employ the Sobel operator to estimate gradients in the image, then use these gradients as weights in an averaging operation. To accelerate the operation, and remove certain unwanted features, integral images are iteratively calculated from the weighted images. This allows us to estimate the illumination component of the images, and in turn the reflectance component as their enhanced ones. A comparative study using a large number of images shows that the proposed algorithm outperforms several state of the art approaches.

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“…The main drawbacks of the approaches mentioned above are the need of knowledge about the light source or a large volume of training data. To overcome this demerit, region-based image preprocessing methods are proposed in [5][6][7]. These methods introduced some noise to make global illumination discontinuous.…”

Section: Introductionmentioning

confidence: 99%

A Robust Illumination Normalization Method Based on Mean Estimation for Face Recognition

Luo

Guan

Zhang

2013

ISRN Machine Vision

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An illumination normalization method for face recognition has been developed since it was difficult to control lighting conditions efficiently in the practical applications. Considering that the irradiation light is of little variation in a certain area, a mean estimation method is used to simulate the illumination component of a face image. Illumination component is removed by subtracting the mean estimation from the original image. In order to highlight face texture features and suppress the impact of adjacent domains, a ratio of the quotient image and its modulus mean value is obtained. The exponent result of the ratio is closely approximate to a relative reflection component. Since the gray value of facial organs is less than that of the facial skin, postprocessing is applied to the images in order to highlight facial texture for face recognition. Experiments show that the performance by using the proposed method is superior to that of state of the arts.

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An illumination normalization model for face recognition under varied lighting conditions

Cited by 34 publications

References 25 publications

Illumination Normalization-Based Face Detection under Varying Illumination

Illumination Normalization-Based Face Detection under Varying Illumination

Illumination Normalization Using Weighted Gradient Integral Images

A Robust Illumination Normalization Method Based on Mean Estimation for Face Recognition

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