Inter-Device Periocular Recognition Under Near-Infrared Light

Michał Włodarczyk, Paweł Krotewicz, Damian Kacperski, Wojciech Sankowski, Kamil Grabowski

Abstract


Periocular biometrics is a relatively new field of

research, and only several publications on this topic can be found

in the literature. It can become a promising feature that can

be used independently or as a complement to other biometrics.

In this work, the recognition rates of periocular biometrics on

a single acquisition device and inter-device database is verified

and the impact of different image sources on the performance of

recognition algorithms is investigated. For this purpose a Near-

Infrared Light database was collected. The database contains

images taken by two acquisition devices. In order to test the

periocular biometric trait, three feature extraction methods are

chosen: Histograms of Oriented Gradients, Local Binary Patterns

and Scale Invariant Feature Transform. The fusion of these

methods is also proposed and it is tested on inter-device database.

The feasibility of applying periocular recognition as an individual

decision module for a biometric system is assessed. Experimental

results yield Equal Error Rate of 17.65% for left eye and 20.54%

for right eye using inter-device database of 640 gallery periocular

images for each eye side taken from 32 different individuals

(20 images per individual for each eye side). These results are

obtained by the optimal weighted sum fusion of the three feature

extraction methods.


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