Illumination invariant face recognition: A survey of passive methods

Face recognition under varying illumination is one of the challenging problems in real-time applications. Numerous methods have been developed by the research community to handle the problem. Existing surveys of methods are either too old or do not cover performance analysis of illumination invariant methods. This paper is more extensive than previous surveys and covers recently developed methods. The paper focuses on passive methods which solve the illumination problem by investigating the visible light images in which the face appearance has been altered by varying illumination. The methods are classified into four broad categories, namely (1) subspace-based statistical methods (2) illumination invariant representation methods, (3) model based methods, (4) other illumination handling methods. The other illumination handling category includes the methods which do not fall under first three categories. Performance analysis and discussion of methods and an evaluation of results is presented to determine the suitability and applicability of the method(s) for specific applications. It is observed from the survey of methods that illumination invariant representation based methods are better in terms of the number of training images required, the simplicity, computational complexity and robustness.