GA-based parameter tuning in finger-vein biometric embedded systems for information security

As concerns about security in networking and communication systems rise with their rapid technology advancements, the need for more reliable and stronger user authentication techniques has also increased. Traditional security methods such as personal identification number, password, and key smart cards are proving to be more and more inadequate, especially in large-scale authentication systems. Hence today, the biometric-based security system is gaining acceptance as an effective tool for providing information security, as can be seen by its deployment in various commercial, public, border control and governmental applications. Each biometric technique has its own merits, but recently, finger-vein biometrics has shown great promise with some key advantages over the other biometrics, which include fingerprint, face, iris and voice. Research has shown that finger-vein biometrics yield very low error equal rates (EER). However, there is more room for improvement. This paper presents a method to optimize finger-vein detection by using genetic algorithms (GA) to fine-tune the image processing parameters in an finger-vein biometric FPGA-based system-on-chip embedded system. The parameters that are tunable include threshold levels and filtering parameters. Experimental results show that the optimization process can successfully reduce the EER from 1.004% to 0.101% on the same biometric system, negating the need for an expert system designer intuition on the image processing parameters.