Illumination problem

About: Illumination problem is a research topic. Over the lifetime, 93 publications have been published within this topic receiving 5859 citations.

A Class Specific Representation Learning for Illumination Tolerant Face Recognition

Abstract: An approach of class specific representation based learning for illumination tolerant face recognition is reported in this paper. Autoencoder based representation and class specific reconstruction along with phase correlation in frequency domain for classification is proposed. Autoencoder based representation is evaluated as very few number of training images are sufficient to handle the entire variation of test face subspace. Phase correlation is used at the classification stage to handle the illumination problem as intensity is the primary concern. This judicial combination of representation and classification shows improved recognition accuracy on benchmark databases. The performance of the proposed approach compared to another state-of-the-art technique on other representation based learning is established with extensive experimental. Advantage of the proposed approach is also shown by the performance analysis with single training image, which is necessary for some real time applications.

Face recognition based on illumination restoration

Abstract: Variations in lighting conditions make face recognition an even more challenging and difficult task In this paper, a novel approach is proposed to handle the illumination problem Our method can restore a face image captured under arbitrary lighting conditions to one with frontal illumination by using a ratio-image and an iterative algorithm The restored images with frontal illumination are used for face recognition by means of PCA Experimental results demonstrate that our method can achieve a higher recognition rate, based on the Yale B and Yale database Moreover, our algorithm has several advantages over other previous algorithms: (1) it does not need to estimate the face surface normals and the light source directions; (2) it does not need many images captured under different lighting conditions for each person, nor a set of bootstrap images that includes many images with different illuminations; and (3) it does not need to detect accurate positions of some facial feature points and to warp the image for alignment, etc

Illumination invariant encrypted face recognition using correlation filter

Abstract: One of the challenging tasks in face recognition is illumination variation. In this paper, we propose an encoding domain correlation technique for face recognition to handle the illumination problem. This technique is based on sparse representation of optical encryption of training images to form unconstrained minimum average correlation energy (UMACE) filter. Averages authentication rates for YaleB and PIE database are increased rather than other methods because illumination variations in the plain face images, do not affect encrypted images with uniform histograms. Another advantage for the proposed method is its high security. Because encrypted version of facial images are used rather than plain images at the receiver.

Illumination problem and absolutely focusing mirrors

Abstract: We consider the illumination and the strong illumination properties for closed bounded regions of Euclidean spaces. These properties are intimately connected with a problem of chaoticity of the corresponding billiards. It is shown that there are only two mechanisms of chaoticity in billiard systems, which are called the mechanism of dispersing and the mechanism of defocusing. Our results show how the regions with different illumination properties should be designed. Especially each focusing mirror in the boundary of a region must be an absolutely focusing one. The notion of absolutely focusing mirrors is a new one in the geometric optic and it plays a key role for the illumination problem.