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.
"Signal, Image and Video Processing" – Springer Journals
Published: Mar 6, 2017
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