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Face recognition is a crucial task in various multimedia applications such assecurity check, credential access and motion sensing games. However, the taskis challenging when an input face is noisy (e.g. poor-condition RGB image) orlacks certain information (e.g. 3D face without color). In this work, wepropose a Multimodal Training Unimodal Test (MTUT) framework for robust faceclassification, which exploits the cross-modality relationship during trainingand applies it as a complementary of the imperfect single modality input duringtesting. Technically, during training, the framework (1) builds bothintra-modality and cross-modality autoencoders with the aid of facialattributes to learn latent embeddings as multimodal descriptors, (2) proposes anovel multimodal embedding divergence loss to align the heterogeneous featuresfrom different modalities, which also adaptively avoids the useless modality(if any) from confusing the model. This way, the learned autoencoders cangenerate robust embeddings in single-modality face classification on teststage. We evaluate our framework in two face classification datasets and twokinds of testing input: (1) poor-condition image and (2) point cloud or 3D facemesh, when both 2D and 3D modalities are available for training. Weexperimentally show that our MTUT framework consistently outperforms tenbaselines on 2D and 3D settings of both datasets.

Unimodal Face Classification with Multimodal Training