Fingerprint matching based on extreme learning machine

Autor: Jucheng Yang, Shouyuan Yang, Shan Juan Xie, Zhijun Fang, Dong Sun Park, Sook Yoon
Rok vydání: 2012
Předmět:
Zdroj: Neural Computing and Applications. 22:435-445
ISSN: 1433-3058
0941-0643
Popis: Considering fingerprint matching as a classification problem, the extreme learning machine (ELM) is a powerful classifier for assigning inputs to their corresponding classes, which offers better generalization performance, much faster learning speed, and minimal human intervention, and is therefore able to overcome the disadvantages of other gradient-based, standard optimization-based, and least squares-based learning techniques, such as high computational complexity, difficult parameter tuning, and so on. This paper proposes a novel fingerprint recognition system by first applying the ELM and Regularized ELM (R-ELM) to fingerprint matching to overcome the demerits of traditional learning methods. The proposed method includes the following steps: effective preprocessing, extraction of invariant moment features, and PCA for feature selection. Finally, ELM and R-ELM are used for fingerprint matching. Experimental results show that the proposed methods have a higher matching accuracy and are less time-consuming; thus, they are suitable for real-time processing. Other comparative studies involving traditional methods also show that the proposed methods with ELM and R-ELM outperform the traditional ones.
Databáze: OpenAIRE
Externí odkaz: