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Iris Matching Algorithm on Many-Core Platforms

Chen Liu, Benjamin Petroski, Guthrie Cordone, Gildo Torres, Stephanie Schuckers
Department of Electrical and Computer Engineering, Clarkson University, Potsdam, New York 13699
2015 IEEE International Symposium on Technologies for Homeland Security (HST’15), 2015

@article{liu2015iris,

   title={Iris Matching Algorithm on Many-Core Platforms},

   author={Liu, Chen and Petroski, Benjamin and Cordone, Guthrie and Torres, Gildo and Schuckers, Stephanie},

   year={2015}

}

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Biometrics matching has been widely adopted as a secure way for identification and verification purpose. However, the computation demand associated with running this algorithm on a big data set poses great challenge on the underlying hardware platform. Even though modern processors are equipped with more cores and memory capacity, the software algorithm still requires careful design in order to utilize the hardware resource effectively. This research addresses this issue by investigating the biometric application on many-core platforms. Biometrics algorithm, specifically Daugman’s iris matching algorithm, is used to benchmark and compare the performance of several many-core platforms. The results show the ability of the iris matching application to efficiently scale and fully exploit the capabilities offered by many-core platforms and provide insights in how to migrate the biometrics computation onto high-performance many-core architectures.
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