Tied Factor Analysis for Face Recognition Across Large Pose Changes

Face recognition algorithms perform very unreliably when the pose of the probe face is different from the stored face: typical feature vectors vary more with pose than with identity. We propose a generative model that creates a one-to-many mapping from an idealized “identity” space to the observed data space. In this identity space, the representation for each individual does not vary with pose. The measured feature vector is generated by a posecontingent linear transformation of the identity vector in the presence of noise. We term this model “tied” factor analysis. The choice of linear transformation (factors) depends on the pose, but the loadings are constant (tied) for a given individual. Our algorithm estimates the linear transformations and the noise parameters using training data. We propose a probabilistic distance metric which allows a full posterior over possible matches to be established. We introduce a novel feature extraction process and investigate recognition performance using the FERET database. Recognition performance is shown to be significantly better than contemporary approaches.