@inproceedings {24,
	title = {Illumination Robust Facial Feature Detection via Decoupled Illumination and Texture Features},
	booktitle = {International Conference on Image Analysis and Recognition 2015},
	year = {2015},
	abstract = {<p>A method for illumination robust facial feature detection on frontal images of the human face is proposed. Illumination robust features are produced from weighted contributions of the texture and illumination components of an image where the illumination is estimated via Bayesian least-squares minimization with the required posterior probability inferred using an adaptive Monte-Carlo sampling approach. This estimate is used to decouple the illumination and texture components, from which Haar-like features are extracted. A weighted aggregate of each component\&$\#$39;s features is then compared with a cascade of pre-trained classifiers for the face, eyes, nose, and mouth. Experimental results against the Yale Face Database B suggest higher sensitivity and F1 score values than current methods while maintaining comparable specificity and accuracy in the presence of non-ideal illumination conditions.</p>
},
	keywords = {illumination robust, image processing, object detection},
	author = {B Chwyl and A Wong and D A. Clausi}
}
@inproceedings {34,
	title = {TIGER: A Texture-Illumination Guided Energy Response Model for Illumination Robust Local Saliency},
	booktitle = {IEEE International Conference on Image Processing},
	year = {2015},
	abstract = {<p>Local saliency models are a cornerstone in image processing and computer vision, used in a wide variety of applications ranging from keypoint detection and feature extraction, to image matching and image representation. However, current models exhibit difficulties in achieving consistent results under varying, non-ideal illumination conditions. In this paper, a novel texture-illumination guided energy response (TIGER) model for illumination robust local saliency is proposed. In the TIGER model, local saliency is quantified by a modified Hessian energy response guided by a weighted aggregate of texture and illumination aspects from the image. A stochastic Bayesian disassociation approach via Monte Carlo sampling is employed to decompose the image into its texture and illumination aspects for the saliency computation. Experimental results demonstrate that higher correlation between local saliency maps constructed from the same scene under different illumination conditions can be achieved using the TIGER model when compared to common local saliency approach, i.e., Laplacian of Gaussian, Difference of Gaussians, and Hessian saliency models.</p>
},
	keywords = {Bayesian estimation, illumination, illumination robust, local features, local saliency model, stochastic, texture},
	author = {B Chwyl and A Chung and F Li and A Wong and D A. Clausi}
}
