On-line unsupervised adaptation for face verification using Gaussian Mixture Models with multiple user models

In this paper, we present an initial study of on-line unsupervised adaptation for face verification. To the authors’ knowledge this is the first study of this type. The key contributions consist of four test scenarios for the BANCA database as well as two novel adaptation strategies that use multiple user models. We show that by using multiple user models for each user, we can perform on-line unsupervised adaptation with a consistent increase in verification performance. Finally, we show that one of the proposed strategies performs better, or as well as the baseline, in four test scenarios at all adaptation thresholds evaluated. This suggests that this strategy is robust against both changing conditions and inexact thresholds.