Abstract
Wavelets have been a prominent image analysis tool over the past decade. Face recognition researchers use it for varied reasons – pre-processing, compression and feature extraction. We refer the reader to [1] for a good review on the theory and applications of wavelets in face recognition. In this chapter we will concentrate on some new transforms that have emerged from the limitations in wavelets. First, we will outline the limitations of wavelets and show how the new image analysis tools overcome them. Next, we review some of the existing work in face recognition that has benefited from using these tools. Finally, we show how these new tools fit into the larger, newly developing arena of signal processing known as Compressive Sampling or Compressed Sensing (CS). We outline how CS can be used for face recognition which certainly will be a new direction in the field of face recognition.
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