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Cross-database non-frontal expression recognition is a very meaningful butrather difficult subject in the fields of computer vision and affect computing.In this paper, we proposed a novel transductive deep transfer learningarchitecture based on widely used VGGface16-Net for this problem. In thisframework, the VGGface16-Net is used to jointly learn an common optimalnonlinear discriminative features from the non-frontal facial expressionsamples between the source and target databases and then we design a noveltransductive transfer layer to deal with the cross-database non-frontal facialexpression classification task. In order to validate the performance of theproposed transductive deep transfer learning networks, we present extensivecrossdatabase experiments on two famous available facial expression databases,namely the BU-3DEF and the Multi-PIE database. The final experimental resultsshow that our transductive deep transfer network outperforms thestate-of-the-art cross-database facial expression recognition methods.

Cross-database non-frontal facial expression recognition based on transductive deep transfer learning