Visual illumination compensation for face images using light mapping matrix

Naderi, S.; Nasiri, Jalal A.; Charkari, Nasrollah Moghadam; Kabir, Ehsanollah

doi:10.1049/iet-ipr.2012.0576

Cited by 3 publications

(3 citation statements)

References 23 publications

(35 reference statements)

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“…Then, based on the IQI, we build the illumination preprocessing method's adaptive parameter adjustment model, which can adjust the parameters of the traditional illumination preprocessing methods according to the illumination conditions. It was demonstrated experimentally in [17] that the LTV model is a particularly good facial illumination preprocessing method and performs better than other common methods such as MSR and SQI. Additionally, the LTV model only has one parameter that must be determined.…”

Section: Adaptive Illumination Preprocessing Based On the Ltv Modelmentioning

confidence: 99%

“…The reflection component is used for face recognition. The multi-scale retinex (MSR) method [13], anisotropic smoothing method [14], self-quotient image (SQI) method [15], log-domain discrete cosine transform (LogDCT) method [16], [17], logarithm total variation (LTV) model [18], and small-and-large-scale (S&L) method [19] belong to this category.…”

Section: Introductionmentioning

confidence: 99%

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An Adaptive Illumination Preprocessing Method for Face Recognition

Ren¹,

Chen²,

Zhang³

et al. 2017

International Journal of Robotics and Automation

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Illumination variation is one of the most important challenges in face recognition, because changes to illumination conditions have a significant effect on performance. However, most illumination preprocessing methods apply the same level of processing to all facial images, without considering their unique illumination conditions. Therefore, the performances of existing preprocessing methods are limited when dealing with varying illumination conditions. In this paper, we propose an adaptive illumination preprocessing method for face recognition, which adaptively preprocesses each face image according to its illumination condition. The proposed method first uses the illumination quality index (IQI) to describe the illumination.Then, we create an adaptive parameter adjustment model for the illumination preprocessing method. Finally, the proposed model adjusts the parameters of the illumination preprocessing method based on the IQI of the facial image, and enhances the preprocessing effect. Our extensive simulation results show that the proposed method can effectively improve the performance of face recognition under varying illumination conditions, when compared with existing methods.

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Section: Adaptive Illumination Preprocessing Based On the Ltv Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Adaptive Illumination Preprocessing Method for Face Recognition

Ren¹,

Chen²,

Zhang³

et al. 2017

International Journal of Robotics and Automation

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“…Pu and Wang [71] proposed a wavelet approach to first denoise the image and then use an entropy measure to decide how to rescale the low-frequency DCT coefficients. Comparably, Naderi et al [72] developed an adaptive method to adjust low-frequency DCT components separately depending on the lighting condition in the image.…”

Section: Discrete Cosine Transformmentioning

confidence: 99%

Addressing the illumination challenge in two‐dimensional face recognition: a survey

Ochoa-Villegas

Nolazco-Flores

Barron-Cano

et al. 2015

IET Computer Vision

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Uncontrolled illumination is one of the most widely researched and most encountered face recognition challenges in recent years. In this study, the authors propose the division of algorithms into two categories: (i) relighting and (ii) unlighting. Relighting methods try to match the probe's illumination conditions using a subset of representative gallery images, while unlighting methods seek to suppress the variations. A total of 64 state-of-the-art methods are summarised and categorised in each of the groups. To make this work concise and easy to follow, they restricted themselves to selected conferences/journals and they limited the number of approaches reviewed. Also, eight past state-of-the-art approaches are used in both identification and verification experiments. However, only significant reported results from all methods were compared and organised in tables. The author's main objective is not to provide an exhaustive analysis of each category, but to present a collection of papers that can be useful in identifying research directions. Results indicate that unlighting methods are a better and a practical solution to address illumination challenges.

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Noise-aided dynamic range compression using selective processing in a statistics-dependent stochastic resonance model

Chouhan

Biswas

2015

2015 Visual Communications and Image Processing (VCIP)

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Visual illumination compensation for face images using light mapping matrix

Cited by 3 publications

References 23 publications

An Adaptive Illumination Preprocessing Method for Face Recognition

An Adaptive Illumination Preprocessing Method for Face Recognition

Addressing the illumination challenge in two‐dimensional face recognition: a survey

Noise-aided dynamic range compression using selective processing in a statistics-dependent stochastic resonance model

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