Fractal face representation and recognition

This paper presents a face representation and recognition scheme based on the theory of fractals. Each face image is represented by its fractal model which is a small collection of transformation parameters. The transformation is carried out once for known face images. For recognition, the input face image is transformed and its fractal model is then compared against the database of fractal models of known faces. Feedforward neural networks are utilized to implement the compression and recognition parts. Some experimental results are presented. The maximum compression ratio obtained for the successful recognition of known faces was observed to be 89:1 (for a compression threshold of 0.002).