Hand Geometry Recognition System
This paper presents a useful biological approach for hand geometrybased recognition systems. Measurable hand geometry such as width, length, and
finger area, were used to generate feature vectors. As useful properties, thirtyfive hand-shaped geometry scales are used. Artificial neural networks are used as
distinct classifiers. The experimental result of all dataset reaches to the
performance of 98.30% as recognition rate
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