Abstract
Two of the most important state-of-the-art challenges in face recognition are: dealing with image acquisition conditions very different between the gallery and the probe set and dealing with large datasets of individuals. In this paper we face both aspects presenting a method which is able to work in “real life” scenarios, in which face images are differently illuminated, can be partially occluded or can show different facial expressions or noise levels. Our proposed system has been tested with datasets of 1000 different individuals, showing performances usually obtained with much smaller gallery sets and much better images. The approach we propose is based on SIFT descriptors, which are known to be robust to different illumination conditions and noise levels. SIFTs are used to automatically detect face regions (mouth area, eye area, etc.). Such regions are then independently compared with the corresponding regions of the gallery images for computing a similarity-based renking of the system’s database.
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