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.

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