A Fast Face Recognition Method Based on Fractal Coding

A Fast Face Recognition Method Based on Fractal Coding Nowadays, many methods for face recognition are proposed and most of them can obtain good results. However, when these methods are simulated on the platform of the PC, it is hard to apply these methods, especially complex ones to practical devices. This paper uses fractal theory to compress face images and improves the encoding speed with the inherent feature of facial symmetry. To improve the performance of Fractal Neighbor Distance (FND), which is a way of ranging, the degree of similarity between encoded images is defined, and a novel method called Fractal Neighbor Distance-based Classification (FNDC) is presented in this paper. The criterion of FNDC is classifying different samples of the same person as a class. Experimental results on Yale, FERET and CMU PIE databases show the effectiveness of FNDC in face recognition. Then we apply the method to i.MX6 which embeds Android operating system. Actual operating results demonstrated the high efficiency of our method in runtime and correct rate. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png "Signal, Image and Video Processing" Springer Journals

A Fast Face Recognition Method Based on Fractal Coding

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Publisher
Springer London
Copyright
Copyright © 2017 by Springer-Verlag London
Subject
Engineering; Signal,Image and Speech Processing; Image Processing and Computer Vision; Computer Imaging, Vision, Pattern Recognition and Graphics; Multimedia Information Systems
ISSN
1863-1703
eISSN
1863-1711
D.O.I.
10.1007/s11760-017-1078-7
Publisher site
See Article on Publisher Site

Abstract

Nowadays, many methods for face recognition are proposed and most of them can obtain good results. However, when these methods are simulated on the platform of the PC, it is hard to apply these methods, especially complex ones to practical devices. This paper uses fractal theory to compress face images and improves the encoding speed with the inherent feature of facial symmetry. To improve the performance of Fractal Neighbor Distance (FND), which is a way of ranging, the degree of similarity between encoded images is defined, and a novel method called Fractal Neighbor Distance-based Classification (FNDC) is presented in this paper. The criterion of FNDC is classifying different samples of the same person as a class. Experimental results on Yale, FERET and CMU PIE databases show the effectiveness of FNDC in face recognition. Then we apply the method to i.MX6 which embeds Android operating system. Actual operating results demonstrated the high efficiency of our method in runtime and correct rate.

Journal

"Signal, Image and Video Processing"Springer Journals

Published: Mar 6, 2017

References

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