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As the identification process is based on the unique patterns of the users, biometrics technologies are expected to provide
highly secure authentication systems. The existing systems using fingerprints or retina patterns are, however, very
vulnerable. One's fingerprints are accessible as soon as the person touches a surface, while a high resolution camera
easily captures the retina pattern. Thus, both patterns can easily be "stolen" and forged. Beside, technical considerations
decrease the usability for these methods. Due to the direct contact with the finger, the sensor gets dirty, which decreases
the authentication success ratio. Aligning the eye with a camera to capture the retina pattern gives uncomfortable feeling.
On the other hand, vein patterns of either a palm of the hand or a single finger offer stable, unique and repeatable
biometrics features.
A fingerprint-based identification system using Cellular Neural Networks has already been proposed by Gao. His system
covers all stages of a typical fingerprint verification procedure from Image Preprocessing to Feature Matching. This
paper performs a critical review of the individual algorithmic steps. Notably, the operation of False Feature Elimination
is applied only once instead of 3 times. Furthermore, the number of iterations is limited to 1 for all used templates.
Hence, the computational need of the feedback contribution is removed. Consequently the computational effort is
drastically reduced without a notable chance in quality. This allows a full integration of the detection mechanism. The
system is prototyped on a Xilinx Virtex II Pro P30 FPGA.
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