Face recognition under camouflage and adverse illumination

This paper presents a method for face identification under adverse conditions by combining regular, frontal face images with facial strain maps using score-level fusion. Strain maps are generated by calculating the central difference method of the optical flow field obtained from each subject´s face during the open mouth expression. Subjects were recorded with and without camouflage under three lighting conditions: normal lighting, low lighting, and strong shadow. Experimental results demonstrate that strain maps are a useful supplemental biométrie in all three adverse conditions, especially in the camouflage condition, where a 30% increase in rank 1 recognition is observed over a baseline PCA-based algorithm.