Boosting-based transfer learning for multi-view head-pose classification from surveillance videos
Radu L. Vieriu, Anoop Kolar Rajagopal, Ramanathan Subramanian, Oswald Lanz, Elisa Ricci, Nicu Sebe, Kalpathi Ramakrishnan · NOT FOUND REPOSITORY (Indian Institute of Science Bangalore) · 2012
This work proposes a boosting-based transfer learning approach for head-pose classification from multiple, low-resolution views. Head-pose classification performance is adversely affected when the source (training) and target (test) data arise from different distributions (due to change in face appearance, lighting, etc). Under such conditions, we employ Xferboost, a Logitboost-based transfer learning framework that integrates knowledge from a few labeled target samples with the source model to effectively minimize misclassifications on the target data. Experiments confirm that the Xferboost framework can improve classification performance by up to 6%, when knowledge is transferred between the CLEAR and FBK four-view headpose datasets. © 2012 EURASIP.