Illumination Invariant Face Recognition Based on Nonsubsampled Contourlet Transform and NeighShrink Denoise

Ma, Yuanyuan; Xia, Suna; Zheng, Gangmin; Ma, Xuehu

doi:10.1007/978-3-642-35286-7_45

Cited by 1 publication

(1 citation statement)

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“…Feature point selection analysis tries to maximise the separability of classes while making the correlation between feature vectors as small as possible. Ma et al [91] proposed an algorithm based on NSCT and the NeighShrink denoising model to obtain an effective illumination representation. NSCT is used to preserve edges and the NeighShrink denoising model is applied to consider the correlation of neighbouring contourlet transform coefficients.…”

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%