Title :
Fractal face representation and recognition
Author :
Kouzani, A.Z. ; He, F. ; Sammut, K.
Author_Institution :
Sch. of Eng., Flinders Univ. of South Australia, Adelaide, SA, Australia
Abstract :
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 utilised 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)
Keywords :
data compression; face recognition; feedforward neural nets; fractals; image coding; image representation; compression ratio; compression threshold; face image; feedforward neural networks; fractal face representation; transformation parameters; Computational modeling; Face detection; Face recognition; Fractals; Image coding; Image databases; Image recognition; Neural networks; Principal component analysis; Shape;
Conference_Titel :
Systems, Man, and Cybernetics, 1997. Computational Cybernetics and Simulation., 1997 IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-4053-1
DOI :
10.1109/ICSMC.1997.638231
