The most important properties of Gabor filtering are related to invariance to illumination, rotation, scale, and translation.
Introduction
By invariance, not only are features meant which are invariant to a set of geomeric transforms, but also methods to perform object detection regardless of pose and imaging conditions using features which are not invariant.
Confusing terminologies: Gabor filter, Gabor expansion, Gabor transform, Gabor jet, Gabor frame, or Gabor wavelet?
As Daugman pointed out, the 2-D Gabor filters are good models of the simple cells in the mammalian visual cortex system.
Survey and overview
The uncertainty can be measured by root mean square (rms) bandwidth: dt, df
The signal which occupies the minimum area dt*df=1/4π is the modulation product of a harmonic oscillation of any frequency with pulse of the form of a probability function.
Gabor expansion
It should be noted that the expansion functions do not have to constitute an orthogonal basis as typically assumed in wavelet or FT, but an unconditional basis, a frame, may succeed as well.
Invariant recognition
The Gabor filters are optimally joint localized in time and frequency, and thus, distortions and noise present in distinct locations, time or frequency, do not significantly interfere with the filter responses.
Examples
Symbol recognition
This first example utilizes a globally computed sum of Gabor responses, corresponding to edge histograms over different orientation, and thus, provides no elegance or novelty as a feature extraction method.