Boosting chromatic information for face recognition
In this paper, chromatic information is integrated with an Adaboost learner to address non linearities in face patterns and illumination variations in training databases for face recognition (FR). An LDA based learner is boosted and the integrated framework is tested on a large database of images having severe pose and illumination variations. The increased dimensionality of color induces a small sample size problem when used with an LDA based learner. The integrated framework is tested on a number of learning scenarios in order to examine this effect. Experimental results show that integrating color into the boosting framework produces a high performing FR system for a range of learning scenarios.