Disentangled Variational Representation for Heterogeneous Face Recognition

Abstract

Visible (VIS) to near infrared (NIR) face matching is a challenging problem due to the significant domain discrepancy between the domains and a lack of sufficient data for training cross-modal matching algorithms. Existing approaches attempt to tackle this problem by either synthesizing visible faces from NIR faces, extracting domain-invariant features from these modalities, or projecting heterogeneous data onto a common latent space for cross-modal matching. In this paper, we take a different approach in which we make use of the Disentangled Variational Representation (DVR) for cross-modal matching. First, we model a face representation with an intrinsic identity information and its within-person variations. By exploring the disentangled latent variable space, a variational lower bound is employed to optimize the approximate posterior for NIR and VIS representations. Second, aiming at obtaining more compact and discriminative disentangled latent space, we impose a minimization of the identity information for the same subject and a relaxed correlation alignment constraint between the NIR and VIS modality variations. An alternative optimization scheme is proposed for the disentangled variational representation part and the heterogeneous face recognition network part. The mutual promotion between these two parts effectively reduces the NIR and VIS domain discrepancy and alleviates over-fitting. Extensive experiments on three challenging NIR-VIS heterogeneous face recognition databases demonstrate that the proposed method achieves significant improvements over the state-of-the-art methods.

arXiv commentAAAI 2019

References in corpus (8)

• Auto-Encoding Variational Bayes
• Disentangling by Factorising
• Deep CORAL: Correlation Alignment for Deep Domain Adaptation
• MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition
• Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis
• Disentangling Factors of Variation via Generative Entangling
• Generative Adversarial Network-based Synthesis of Visible Faces from Polarimetric Thermal Faces
• Shared Representation Learning for Heterogeneous Face Recognition

Cited by in corpus (4)

• Relational Deep Feature Learning for Heterogeneous Face Recognition
• Joint Feature Distribution Alignment Learning for NIR-VIS and VIS-VIS Face Recognition
• Synthesis of High-Quality Visible Faces from Polarimetric Thermal Faces using Generative Adversarial Networks
• Multi-Margin based Decorrelation Learning for Heterogeneous Face Recognition