Super resolution based face recognition: do we need training image set?

Nadia Al-Hassan; Harin Sellahewa; Sabah A. Jassim

doi:10.1117/12.2027018

28 May 2013 Super resolution based face recognition: do we need training image set?

Nadia Al-Hassan, Harin Sellahewa, Sabah A. Jassim

Proceedings Volume 8755, Mobile Multimedia/Image Processing, Security, and Applications 2013; 87550P (2013) https://doi.org/10.1117/12.2027018
Event: SPIE Defense, Security, and Sensing, 2013, Baltimore, Maryland, United States

Abstract

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.

Citation Download Citation

Nadia Al-Hassan, Harin Sellahewa, and Sabah A. Jassim "Super resolution based face recognition: do we need training image set?", Proc. SPIE 8755, Mobile Multimedia/Image Processing, Security, and Applications 2013, 87550P (28 May 2013); https://doi.org/10.1117/12.2027018

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