Is there a better Fourier Transform-based algorithm to use in Image recognition I have been trying to use the Fourier transform in recognizing images of the same size 200x400.

I have tried many different ways to do that such as:


Performing the Fourier on the full image and then try to match it with the ones in the database. the accuracy rate was very bad.
Performing the Fourier on the full image and then take only the
sub-matrix contained in centre of the image and then match it with
the ones in the database. the accuracy rate was also very bad.
Performing the Fourier on the full image and then take only the
horizontal and vertical vector that intersect in the centre of the
image, and then match it with the ones in the database. also, the
accuracy rate was very bad.


Is there any better algorithm that might help me solve this problem?

Thank you very much
The Fourier Transform is good for picking up some similarities between images: 


Shifts in $x$ and $y$ are just phase changes in the Fourier domain.
Rotations in the $x-y$ plane cause rotations in the Fourier domain.


So, if this is what you are trying to achieve, then it makes sense.

See this paper for a performance analysis of doing this using the following algorithm