  In this paper, we consider the challenge of face morphing attacks, which
substantially undermine the integrity of face recognition systems such as those
adopted for use in border protection agencies. Morph detection can be
formulated as extracting fine-grained representations, where local
discriminative features are harnessed for learning a hypothesis. To acquire
discriminative features at different granularity as well as a decoupled
spectral information, we leverage wavelet domain analysis to gain insight into
the spatial-frequency content of a morphed face. As such, instead of using
images in the RGB domain, we decompose every image into its wavelet sub-bands
using 2D wavelet decomposition and a deep supervised feature selection scheme
is employed to find the most discriminative wavelet sub-bands of input images.
To this end, we train a Deep Neural Network (DNN) morph detector using the
decomposed wavelet sub-bands of the morphed and bona fide images. In the
training phase, our structured group sparsity-constrained DNN picks the most
discriminative wavelet sub-bands out of all the sub-bands, with which we
retrain our DNN, resulting in a precise detection of morphed images when
inference is achieved on a probe image. The efficacy of our deep morph detector
which is enhanced by structured group lasso is validated through experiments on
three facial morph image databases, i.e., VISAPP17, LMA, and MorGAN.
