ABSTRACT
While there have been varying and significant levels of performance achieved through the use of spatial 2D image data, the use of a frequency domain representation sometimes achieves better performance for the face recognition tasks. The use of the Fourier transforms allow to quickly and easily obtain raw frequency data which are significantly more discriminating (after appropriate data manipulation) than the raw spatial data, from which it is derived. One can further increase the discrimination ability through additional and specific feature extraction algorithms intended for use in the frequency domain. In the majority of cases, correlation filters [132] are used to achieve desired performances due to several advantages, such as 1) it has built-in shift invariance, 2) correlation filters are based on integration operation and thus offer graceful degradation of any impairment to the test face image, 3) correlation filters can be designed to exhibit attributes such as noise tolerance and high ability for discrimination and 4) finally design of correlation filter is derived from closed form expressions and thus physically realizable.
