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Heterogeneous face recognition is a challenging task due to the largemodality discrepancy and insufficient cross-modal samples. Most existing worksfocus on discriminative feature transformation, metric learning and cross-modalface synthesis. However, the fact that cross-modal faces are always coupled bydomain (modality) and identity information has received little attention.Therefore, how to learn and utilize the domain-private feature anddomain-agnostic feature for modality adaptive face recognition is the focus ofthis work. Specifically, this paper proposes a Feature Aggregation Network(FAN), which includes disentangled representation module (DRM), feature fusionmodule (FFM) and adaptive penalty metric (APM) learning session. First, in DRM,two subnetworks, i.e. domain-private network and domain-agnostic network arespecially designed for learning modality features and identity features,respectively. Second, in FFM, the identity features are fused with domainfeatures to achieve cross-modal bi-directional identity feature transformation,which, to a large extent, further disentangles the modality information andidentity information. Third, considering that the distribution imbalancebetween easy and hard pairs exists in cross-modal datasets, which increases therisk of model bias, the identity preserving guided metric learning withadaptive hard pairs penalization is proposed in our FAN. The proposed APM alsoguarantees the cross-modality intra-class compactness and inter-classseparation. Extensive experiments on benchmark cross-modal face datasets showthat our FAN outperforms SOTA methods.

Domain Private and Agnostic Feature for Modality Adaptive Face Recognition