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

Abstract

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.

NIRFaceNet: A Convolutional Neural Network for Near-Infrared Face Identification

“…Up to now, many methods [1][2][3][4][5] have been used in order to obtain higher recognition accuracy. However, most of them are concentrated on recognizing facial images in the visible spectrum, which are vulnerable to changes in environmental illumination [6][7][8][9].…”

An Advanced Framework for Critical Infrastructure Protection Using Computer Vision Technologies

“…Surveys that discuss deep face recognition have singled out face recognition as an individual discipline rather than a collection of components adopted from different studies. These surveys generally discuss the face recognition pipeline: face pre-processing, network, loss function, and face classification [ 17 , 19 ] or discuss a single aspect of face recognition such as 3-D face recognition [ 28 ], illumination face recognition [ 20 ] or pose invariant face recognition [ 9 ]. Although these surveys are important and provide an excellent basis for the analysis of the state-of-the-art in the field, they do not provide conclusive comparisons or analysis of the underlying network architectures.…”

Adaptive Image Rendering Using a Nonlinear Mapping-Function-Based Retinex Model

“…The high performance and miniaturization of image sensors make it possible for image information to be used in various applications, such as mobile platforms, recognition systems, and security systems [ 1 , 2 ]. However, low contrast coming from an absent light source leads to the degradation of image quality, so the performance of the application system may be unsatisfactory [ 3 ]. In order to solve the low-contrast problem, many simple approaches, such as histogram equalization, gamma correction, and auto exposure, have been widely used [ 4 ].…”