Deep face recognition with clustering based domain adaptation

TL;DR

This paper introduces a clustering-based domain adaptation method for deep face recognition that effectively aligns source and target domains without shared classes, improving recognition accuracy in real-world scenarios.

Contribution

It proposes a novel unsupervised domain adaptation approach that combines global domain discrepancy minimization with local clustering to enhance face recognition performance.

Findings

Achieves state-of-the-art results on GBU dataset.

Effectively adapts to target domain without labeled data.

Improves recognition accuracy across multiple databases.

Abstract

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.