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Exploiting Partial Information for Robust Speech and Speaker Recognition

Nova Publishers, Hauppauge, NY
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ILLUMINATION INVARIANT FACIAL RECOGNITION USING A PIECEWISE-CONSTANT LIGHTING MODEL ILLUMINATION INVARIANT FACIAL RECOGNITION USING A PIECEWISE-CONSTANT LIGHTING MODEL Niall McLaughlin, Ji Ming, Danny Crookes Institute of Electronics, Communications and Information Technology Queen’s University Belfast, Belfast BT3 9DT, United Kingdom ABSTRACT In this paper we demonstrate a simple and novel illumination model that can be used for illumination invariant facial recognition. This model requires no prior knowledge of the illumination conditions and can be used when there is only a single training image per- person. The proposed illumination model separates the effects of illumination over a small area of the face into two components; an additive component modelling the mean illumination and a multi- plicative component, modelling the variance within the facial area. Illumination invariant facial recognition is performed in a piecewise manner, by splitting the face image into blocks, then normalizing the illumination within each block based on the new lighting model. The assumptions underlying this novel lighting model have been verified on the YaleB face database. We show that magnitude 2D Fourier features can be used as robust facial descriptors within the new light- ing model. Using only a single training image per-person, our new method achieves high (in most cases 100%) identification accuracy on the YaleB, extended YaleB and CMU-PIE face databases. Index Terms— facial recognition, illumination invariance, lighting model, limited training data 1. INTRODUCTION The problem of facial recognition under realistic lighting has re- cently received much research attention and several major ap- proaches have been explored including: illumination modelling, photometric normalisation and illumination invariant representa- tions. In this paper we study facial identification given unknown realistic lighting, with a single training image per-person. The standard theory of facial illumination is the

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