From the last ancient the significance of biometrics has been truly configured
due to its important in the every day lifestyles that begins from civilian
functions to military actions as well as commercial applications. A Footprint
cognizance is a one type of the excellent personal identity based totally on
biometric measures. The intention of this research is to sketch a desirable and
reliable left foot tip biometric system entitled (LFBS). This paper provides a
robust varied technique which connects between two important technology
techniques they are Image Processing and Artificial Intelligent technique via
Bird Swarm Optimization Algorithm (BSA) to apprehend the human footprint.
The use of (BSA) enhances the overall performance and the quality of the
outcomes produced from the proposed biometric application via function
selection. The chosen facets were once handled as the top of the line attribute
set in places of characteristic collection size. The visual database was once
developed through capturing life RGB footprint images [1]. Freeman chain
code was used with footprint template (black and white image), then statistical
values which represent the footprint features was extracted. These aspects have
been extracted from every image and saved in Excel file to be entered into the
Bird Swarm Algorithm. The experimental effects exhibit that our algorithm
estimates, terrific consequences with a tiny feature set in evaluation with
different algorithms. On the other hand experimental about 100% accuracy in
relation with different papers on the same field. Results show that our
algorithm achieves well-organized and precise result.
due to its important in the every day lifestyles that begins from civilian
functions to military actions as well as commercial applications. A Footprint
cognizance is a one type of the excellent personal identity based totally on
biometric measures. The intention of this research is to sketch a desirable and
reliable left foot tip biometric system entitled (LFBS). This paper provides a
robust varied technique which connects between two important technology
techniques they are Image Processing and Artificial Intelligent technique via
Bird Swarm Optimization Algorithm (BSA) to apprehend the human footprint.
The use of (BSA) enhances the overall performance and the quality of the
outcomes produced from the proposed biometric application via function
selection. The chosen facets were once handled as the top of the line attribute
set in places of characteristic collection size. The visual database was once
developed through capturing life RGB footprint images [1]. Freeman chain
code was used with footprint template (black and white image), then statistical
values which represent the footprint features was extracted. These aspects have
been extracted from every image and saved in Excel file to be entered into the
Bird Swarm Algorithm. The experimental effects exhibit that our algorithm
estimates, terrific consequences with a tiny feature set in evaluation with
different algorithms. On the other hand experimental about 100% accuracy in
relation with different papers on the same field. Results show that our
algorithm achieves well-organized and precise result.
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