Actas de congresos
Ocular Recognition Using Deep Features for Identity Authentication
Fecha
2020-01-01Registro en:
Proceedings Of The 2020 International Conference On Systems, Signals And Image Processing (iwssip), 27th Edition. New York: Ieee, p. 155-160, 2020.
2157-8672
WOS:000615731300028
Autor
Universidade Estadual Paulista (Unesp)
Institución
Resumen
Recently, ocular biometrics has been gaining importance in Biometrics due to the poor performance obtained in some cases by biometric systems based on characteristics of the whole face. This paper presents a new method for person authentication based on ocular deep features, which are extracted from the ocular region of the face by using a very deep CNN (Convolutional Neural Network). Another interesting aspect of our method is that, instead of using directly the deep features as input for the authentication system, it uses the difference between the probe and gallery deep features. So, our method adopts a pairwise strategy. A binary support vector machine is trained to determine whether a given difference vector is genuine or impostor. The proposed new method based on such pairwise strategy was evaluated using the ocular left set of the UBIPr dataset and five pre-trained CNN architectures. When using the pre-trained VGG-Face the proposed method obtained a state-of-the-art result (3.18% of Equal Error Rate).