  Despite great progress in face recognition tasks achieved by deep convolution
neural networks (CNNs), these models often face challenges in real world tasks
where training images gathered from Internet are different from test images
because of different lighting condition, pose and image quality. These factors
increase domain discrepancy between training (source domain) and testing
(target domain) database and make the learnt models degenerate in application.
Meanwhile, due to lack of labeled target data, directly fine-tuning the
pre-learnt models becomes intractable and impractical. In this paper, we
propose a new clustering-based domain adaptation method designed for face
recognition task in which the source and target domain do not share any
classes. Our method effectively learns the discriminative target feature by
aligning the feature domain globally, and, at the meantime, distinguishing the
target clusters locally. Specifically, it first learns a more reliable
representation for clustering by minimizing global domain discrepancy to reduce
domain gaps, and then applies simplified spectral clustering method to generate
pseudo-labels in the domain-invariant feature space, and finally learns
discriminative target representation. Comprehensive experiments on widely-used
GBU, IJB-A/B/C and RFW databases clearly demonstrate the effectiveness of our
newly proposed approach. State-of-the-art performance of GBU data set is
achieved by only unsupervised adaptation from the target training data.
