Human and automatic face recognition: a comparison across image formats

Human subjects perform poorly at matching different images of unfamiliar faces. When images are taken by different capture devices (cameras), matching is difficult for human perceivers and also for automatic systems. We test an automatic face recognition system based on principal components analysis (PCA) and compare its performance with that of human subjects tested on the same set of images. A number of variants of the PCA system are compared, using different matching metrics and different numbers of components. PCA performance critically depends on the choice of distance metric, with a Mahalanobis metric consistently outperforming a Euclidean metric. Under optimal conditions, the automatic PCA system exceeds human performance on the same images. We hypothesise that unfamiliar face recognition may be mediated by processes corresponding to rather simple functions of the inputs.