  This paper proposes a new approach for face verification, where a pair of
images needs to be classified as belonging to the same person or not. This
problem is relatively new and not well-explored in the literature. Current
methods mostly adopt techniques borrowed from face recognition, and process
each of the images in the pair independently, which is counter intuitive. In
contrast, we propose to extract cross-image features, i.e. features across the
pair of images, which, as we demonstrate, is more discriminative to the
similarity and the dissimilarity of faces. Our features are derived from the
popular Haar-like features, however, extended to handle the face verification
problem instead of face detection. We collect a large bank of cross-image
features using filters of different sizes, locations, and orientations.
Consequently, we use AdaBoost to select and weight the most discriminative
features. We carried out extensive experiments on the proposed ideas using
three standard face verification datasets, and obtained promising results
outperforming state-of-the-art.
