Illumination Invariant Face Recognition: A Survey

Zou, Xuan; Kittler, Josef; Messer, Kieron

doi:10.1109/btas.2007.4401921

Cited by 189 publications

(97 citation statements)

References 38 publications

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“…Face detection is highly related to face recognition and numerous face illumination normalization methods were developed to tackle illumination problem in face recognition [33]- [35]. In this paper, we will investigate four face illumination normalization techniques for face detection.…”

Section: Face Illumination Normalization Techniquesmentioning

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: Face Illumination Normalization Techniquesmentioning

confidence: 99%

Illumination Normalization-Based Face Detection under Varying Illumination

Yao

Nagahashi

Aoki

2014

IEICE Trans. Inf. & Syst.

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“…Next, we would like to prove that OWF is only related to R(x,y) and verify that OWF can represent faces in an illumination insensitive way. According to (3), each of the directional face images can be rewritten as …”

Section: International Journal Of Advances Inmentioning

confidence: 99%

“…However, in real face recognition applications, varying illumination tends to significantly affect the appearance of faces and leads to unsatisfactory performance of a face recognition system [1], [2]. To solve the illumination problem of face recognition, numerous methods were proposed [3]- [7]. Most of the existing methods could be sorted into one of the three categories: traditional image processing techniques, face model learning based methods, and illumination insensitive face representation methods.…”

Section: Introductionmentioning

confidence: 99%

Illumination Insensitive Face Representation for Face Recognition Based on Modified Weberface

Yao¹,

Nagahashi²

2013

IJAET

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“…The principal component analysis (PCA) (Turk & Pentland, 1991) and linear discriminant analysis (LDA) (Etemad & Chellappa, 1997;Belhumeur et al, 1997) can be classified to the statistical modeling. In physical modeling, the model is based on the assumption of certain surface reflectance properties, such as Lambertian surface (Zou et al, 2007). The famous Illumination Cone, 9D linear subspace and nine point lights all belong to the illumination variation modeling.…”

Section: Face and Illumination Modellingmentioning

confidence: 99%

“…The common representations include edge map, image intensity derivatives and Gabor-like filtering image (Adini et al, 1997). However, the recognition experiment on a face database with lighting variation indicated that none of these representations was sufficient by itself to overcome the image variation due to the change of illumination direction (Zou et al, 2007). Recently, quotient-image-based methods are reported to be a simple and efficient solution to illumination variances and have become one active research direction.…”

Section: Illumination Invariant Feature Extractionmentioning

confidence: 99%