Object recognition using fractal neighbor distance: eventual convergence and recognition rates

Fractal image coding has recently been used to perform object recognition, in particular human face recognition. It was shown that the transformations resulting from fractal image coding has invariant properties that can be exploited for recognition. Furthermore, the contractivity factor of a fractal code, which can be used to determine convergence using one code iteration, has a direct effect on the recognition rate. This paper investigates how this rate is affected by the eventual contractivity factor, which is an indicator of guaranteed convergence after more than one iteration of the fractal code. We demonstrate this by ensuring eventual convergence while permitting the contractivity factor to possess values larger than one the recognition rates can be improved. Experiments were performed on the ORL face database and an improved error rate of 1.1% was obtained. We also present a novel method for calculating the eventual contractivity factor for a general class of fractal codes.