  Periocular biometric, or peripheral area of ocular, is a collaborative
alternative to face, especially if a face is occluded or masked. In practice,
sole periocular biometric captures least salient facial features, thereby
suffering from intra-class compactness and inter-class dispersion issues
particularly in the wild environment. To address these problems, we transfer
useful information from face to support periocular modality by means of
knowledge distillation (KD) for embedding learning. However, applying typical
KD techniques to heterogeneous modalities directly is suboptimal. We put
forward in this paper a deep face-to-periocular distillation networks, coined
as cross-modal consistent knowledge distillation (CM-CKD) henceforward. The
three key ingredients of CM-CKD are (1) shared-weight networks, (2) consistent
batch normalization, and (3) a bidirectional consistency distillation for face
and periocular through an effectual CKD loss. To be more specific, we leverage
face modality for periocular embedding learning, but only periocular images are
targeted for identification or verification tasks. Extensive experiments on six
constrained and unconstrained periocular datasets disclose that the
CM-CKD-learned periocular embeddings extend identification and verification
performance by 50% in terms of relative performance gain computed based upon
face and periocular baselines. The experiments also reveal that the
CM-CKD-learned periocular features enjoy better subject-wise cluster
separation, thereby refining the overall accuracy performance.
