Recognizing Expression Variant Faces from a Single Sample Image per Class

Although important contributions on face recognition have been recently reported, few are focused on how to robustly recognize expression variant faces from as little as one single training sample per class. Since learning cannot generally be applied when only one sample per class is available, matching techniques (distance measures) are usually employed instead (e.g. correlations). However, distance measures generally attempt to match all features with equal importance (weighting), because not only it is difficult to know which features are more useful (for classification), but when or under which circumstances this happens. For example, when recognizing faces in the original image space (e.g. using the Euclidean distance–correlation), it is not known which pixels are more and which are less appropriate to be used. In this contribution, we use the optical flow between the testing and sample images as a measure of how good each pixel is. Pixels that have a small flow will have high weights, pixels with a large flow will have small weights. Our experimental results show that the method proposed in this contribution outperforms the classical Euclidean distance (correlation) measure and the PCA approach.