Personalized face reference from video: Key-face selection and feature-level fusion

Face recognition from video in uncontrolled environments is an active research field that received a growing attention recently. This was mainly driven by the wide range of applications and the availability of large databases. This work presents an approach to create a robust and discriminant reference face model from video enrollment data. The work focuses on two issues, first is the key faces selection from video sequences. The second is the feature-level fusion of the key faces. The proposed fusion approaches focus on inducing subject specific feature weighting in the reference face model. Quality based sample weighting is also considered in the fusion process. The proposed approach is evaluated under different sittings on the YouTube Faces data-base and the performance gained by the proposed approach is shown in the form of EER values and ROC curves.