Domain Adaptation on Correlated subspace for Unseen Multiview Headpose Classification
Anoop Kolar Rajagopal, K.R. Ramakrishnan · 2014
Head pose classification from images acquired from far field cameras is a challenging problem because of the low resolution, blur and noise due to subject movements. Further there exists a domain shift in head pose classification between training (source) and testing (target) images. Also more often the head poses in the target set may not exist in the source set and acquiring sufficient samples for training is quite expensive. In this paper, we propose a novel framework to address multi-view unseen head pose classification where the target set belongs to a different domain and has more classes than in the source. A correlated subspace is first derived using Canonical Correlation Analysis (CCA) between corresponding head poses in the source (stationary subjects) and target set (moving subjects). A distance based Domain Adaptation technique is then used in the correlation subspace for classification of unseen head pose in the target set. Experimental results confirm the effectiveness of our approach in improving the classification performance over the state-of-art.