Disentangled representations have been commonly adopted to Age-invariant Face
Recognition (AiFR) tasks. However, these methods have reached some limitations
with (1) the requirement of large-scale face recognition (FR) training data
with age labels, which is limited in practice; (2) heavy deep network
architecture for high performance; and (3) their evaluations are usually taken
place on age-related face databases while neglecting the standard large-scale
FR databases to guarantee its robustness. This work presents a novel Attentive
Angular Distillation (AAD) approach to Large-scale Lightweight AiFR that
overcomes these limitations. Given two high-performance heavy networks as
teachers with different specialized knowledge, AAD introduces a learning
paradigm to efficiently distill the age-invariant attentive and angular
knowledge from those teachers to a lightweight student network making it more
powerful with higher FR accuracy and robust against age factor. Consequently,
AAD approach is able to take the advantages of both FR datasets with and
without age labels to train an AiFR model. Far apart from prior distillation
methods mainly focusing on accuracy and compression ratios in closed-set
problems, our AAD aims to solve the open-set problem, i.e. large-scale face
recognition. Evaluations on LFW, IJB-B and IJB-C Janus, AgeDB and
MegaFace-FGNet with one million distractors have demonstrated the efficiency of
the proposed approach. This work also presents a new longitudinal face aging
(LogiFace) database for further studies in age-related facial problems in
future.Comment: arXiv admin note: substantial text overlap with arXiv:1905.1062