This paper is concerned with face recognition under uncontrolled condition, e.g. at a distance surveillance scenarios, and
post-rioting forensic, whereby captured face images are severely degraded/blurred and of low-resolution. This is a tough
challenge due to many factors including capturing conditions. We present the results of our investigations into recently
developed Compressive Sensing (CS) theory to develop scalable face recognition schemes using a variety of overcomplete
dictionaries that construct super-resolved face images from any input low-resolution degraded face image. We
shall demonstrate that deterministic as well as non-deterministic dictionaries that do not involve the use of face image
information but satisfy some form of the Restricted Isometry Property used for CS can achieve face recognition accuracy
levels, as good as if not better than those achieved by dictionaries proposed in the literature, that are learned from face
image databases using elaborate procedures. We shall elaborate on how this approach helps in crime fighting and
terrorism.
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