Abstract:

This paper presents a new method for face recognition which learns a face similarity measure from example image pairs. A set of computationally efficient "rectangle" features are described which act on pairs of input images. The features compare regions within the input images at different locations, scales, and orientations. The AdaBoost algorithm is used to train the face similarity function by selecting features. Given a large face database, the set of face pairs is too large for effective training. We present a sampling procedure which selects a training subset based on the AdaBoost example weights. Finally, we show state of the art results on the FERET set of faces as well as a more challenging set of faces collected at our lab.