  We propose a method to address challenges in unconstrained face detection,
such as arbitrary pose variations and occlusions. First, a new image feature
called Normalized Pixel Difference (NPD) is proposed. NPD feature is computed
as the difference to sum ratio between two pixel values, inspired by the Weber
Fraction in experimental psychology. The new feature is scale invariant,
bounded, and is able to reconstruct the original image. Second, we propose a
deep quadratic tree to learn the optimal subset of NPD features and their
combinations, so that complex face manifolds can be partitioned by the learned
rules. This way, only a single soft-cascade classifier is needed to handle
unconstrained face detection. Furthermore, we show that the NPD features can be
efficiently obtained from a look up table, and the detection template can be
easily scaled, making the proposed face detector very fast. Experimental
results on three public face datasets (FDDB, GENKI, and CMU-MIT) show that the
proposed method achieves state-of-the-art performance in detecting
unconstrained faces with arbitrary pose variations and occlusions in cluttered
scenes.
