Heterogeneous Face Recognition using Inter-Session Variability Modelling
The task of Heterogeneous Face Recognition consists in to match face images that were sensed in different modalities, such as sketches to photographs, thermal images to photographs or near infrared to photographs. In this preliminary work we introduce a novel and generic approach based on Inter-session Variability Modelling to handle this task. The experimental evaluation conducted with two dif- ferent image modalities showed an average rank-1 identification rates of 96.93% and 72.39% for the CUHK-CUFS (Sketches) and CASIA NIR-VIS 2.0 (Near infra-red) respectively. This work is totally reproducible and all the source code for this approach is made publicly available.
Record created on 2016-05-19, modified on 2016-08-09