Enhancement of an Automatic Fingerprint Identification System Using a Genetic Algorithm and Genetic Programming

Author(s):  
Wannasak Wetcharaporn ◽  
Nachol Chaiyaratana ◽  
Sanpachai Huvanandana
2015 ◽  
Vol 734 ◽  
pp. 642-645
Author(s):  
Yan Hui Liu ◽  
Zhi Peng Wang

According to the problem that the letters identification is not high accuracy using neural networks, in this paper, an optimal neural network structure is designed based on genetic algorithm to optimize the number of hidden layer. The English letters can be identified by optimal neural network. The results obtained in the genetic programming optimizations are very satisfactory. Experiments show that the identification system has higher accuracy and achieved good ideal letters identification effect.


2021 ◽  
Vol 2 (3) ◽  
Author(s):  
Uttam U. Deshpande ◽  
V. S. Malemath ◽  
Shivanand M. Patil ◽  
Sushma. V. Chaugule

2016 ◽  
Vol 17 (8) ◽  
pp. 766-780 ◽  
Author(s):  
Yun-xiang Zhao ◽  
Wan-xin Zhang ◽  
Dong-sheng Li ◽  
Zhen Huang ◽  
Min-ne Li ◽  
...  

2012 ◽  
Vol 468-471 ◽  
pp. 920-923
Author(s):  
Ya Ping Bao ◽  
Li Liu ◽  
Yuan Wang ◽  
Qian Song

This paper introduced a fast fingerprint identification system based on TMS320VC5416 DSP chip and MBF200 solidity fingerprint sensor. It precipitates fingerprint identification device developing into the direction of miniaturization, embedded and automatic.It recommends fingerprint identification system hardware and software design and the main system processing flow, aim at fingerprint identification arithmetic, the influence of system operation speed is being researched at the same time. High-speed data acquisition system is been built in order to achieve a DSP fingerprint identification system with high efficiency and low cost.


Author(s):  
Baddrud Zaman Laskar ◽  
Swanirbhar Majumder

Gene expression programming (GEP) introduced by Candida Ferreira is a descendant of genetic algorithm (GA) and genetic programming (GP). It takes the advantage of both the optimization and search technique based on genetics and natural selection as GA and its programmatic Darwinian counterpart GP. It is gaining popularity because; it has to some extent eradicated the ‘cons' of both while keeping in the ‘pros'. It is still a new technique not much explored since its introduction in 2001. In this chapter both GA and GP is first discussed followed by the elaborate discussion of GEP. This is followed up by the discussion on research work done is different fields using GEP as a tool followed up by GEP architectures. Finally, here GEP has been used for detection of age from facial features as a soft computing based optimization problem using genetic operators.


IEEE Expert ◽  
1995 ◽  
Vol 10 (3) ◽  
pp. 11-15 ◽  
Author(s):  
L.M. Howard ◽  
D.J. D'Angelo

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