Quality-dependent view transformation model for cross-view gait recognition

View difference is a factor that degrades the accuracy of gait recognition. A solution to reducing accuracy degradation is applying a view transformation model (VTM) that encodes a joint subspace of multi-view gait features trained from multiple training subjects. In the VTM framework, once an intrinsic vector of a test subject in the joint subspace is estimated from a gait feature with a source view (e.g. probe view), a gait feature with a destination view (e.g. gallery view) is generated for the same-view matching. Although this family of methods can improve the total accuracy, the quality of generated gait features depends on a test gait feature, and may be relevant to the accuracy of gait recognition. We therefore propose a method of incorporating the quality measure of the generated gait feature into the VTM framework. We employ the projection error into the joint subspace as the quality measure. A posterior probability is then computed by incorporating the quality measure. The accuracy evaluation against a subset of a public database collected from 1,912 subjects shows that the proposed method further improves the accuracy.