A Novel Local Illumination Normalization Approach for Face Recognition

Lian, Zhichao; Er, Meng Joo; Li, Juekun

doi:10.1007/978-3-642-21090-7_41

Cited by 8 publications

(2 citation statements)

References 5 publications

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“…Mendez‐Vázquez et al [23] estimated the compensation term by smoothing the image from the low‐frequency part using a local DCT. Lian et al [73] removed the effect of noise by discarding high‐frequency DCT coefficients. After that, a local relation map (LRM) is employed.…”

Section: Unlightingmentioning

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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Section: Unlightingmentioning

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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show abstract

“…Recently, we investigated a novel illumination invariant feature local relation map (LRM), yielding good results that were published in the ISNN 2011 conference [8]. The main idea of the LRM is based on the local properties of facial images and it has a much lower computational load compared to the similar LN method [6].…”

Section: Introductionmentioning

confidence: 99%

Local Relation Map: A Novel Illumination Invariant Face Recognition Approach

Lian

Joo

2012

International Journal of Advanced Robotic Systems

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In this paper, a novel illumination invariant face recognition approach is proposed. Different from most existing methods, an additive term as noise is considered in the face model under varying illuminations in addition to a multiplicative illumination term. High frequency coefficients of Discrete Cosine Transform (DCT) are discarded to eliminate the effect caused by noise. Based on the local characteristics of the human face, a simple but effective illumination invariant feature local relation map is proposed. Experimental results on the Yale B, Extended Yale B and CMU PIE demonstrate the outperformance and lower computational burden of the proposed method compared to other existing methods. The results also demonstrate the validity of the proposed face model and the assumption on noise.

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An Illumination Invariant Face Recognition Scheme to Combining Normalized Structural Descriptor with Single Scale Retinex

Zhou

2013

Biometric Recognition

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A Novel Local Illumination Normalization Approach for Face Recognition

Cited by 8 publications

References 5 publications

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

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

Local Relation Map: A Novel Illumination Invariant Face Recognition Approach

An Illumination Invariant Face Recognition Scheme to Combining Normalized Structural Descriptor with Single Scale Retinex

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