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Cross-database non-frontal facial expression recognition based on transductive deep transfer learning
Computer Vision and Pattern Recognition · Submitted 30 Nov 2018
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim The final experimental results show that our transductive deep transfer network outperforms the state-of-the-art cross-database facial expression recognition methods.
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The abstract, sentence by sentence
Cross-database non-frontal expression recognition is a very meaningful but rather difficult subject in the fields of computer vision and affect computing. In this paper, we proposed a novel transductive deep transfer learning architecture based on widely used VGGface16-Net for this problem. In this framework, the VGGface16-Net is used to jointly learn an common optimal nonlinear discriminative features from the non-frontal facial expression samples between the source and target databases and then we design a novel transductive transfer layer to deal with the cross-database non-frontal facial expression classification task. In order to validate the performance of the proposed transductive deep transfer learning networks, we present extensive crossdatabase experiments on two famous available facial expression databases, namely the BU-3DEF and the Multi-PIE database. The final experimental results show that our transductive deep transfer network outperforms the state-of-the-art cross-database facial expression recognition methods.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Cross-database non-frontal expression recognition is a very meaningful but rather difficult subject in the fields of computer vision and affect computing. | background 1.00 | 0.05 | 0.02 | asserted 1.00 |
| s1 | In this paper, we proposed a novel transductive deep transfer learning architecture based on widely used VGGface16-Net for this problem. | contribution 0.93 | 0.91 | 0.04 | asserted 0.99 |
| s2 | In this framework, the VGGface16-Net is used to jointly learn an common optimal nonlinear discriminative features from the non-frontal facial expression samples between the source and target databases and then we design a novel transductive transfer layer to deal with the cross-database non-frontal facial expression classification task. | method 0.99 | 0.89 | 0.04 | asserted 0.99 |
| s3 | In order to validate the performance of the proposed transductive deep transfer learning networks, we present extensive crossdatabase experiments on two famous available facial expression databases, namely the BU-3DEF and the Multi-PIE database. | method 0.89 | 0.95 | 0.03 | asserted 0.90 |
| s4 | The final experimental results show that our transductive deep transfer network outperforms the state-of-the-art cross-database facial expression recognition methods. | result 1.00 | 0.96 | 0.93 | asserted 1.00 |
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The paper
Original abstract
Cross-database non-frontal expression recognition is a very meaningful but rather difficult subject in the fields of computer vision and affect computing. In this paper, we proposed a novel transductive deep transfer learning architecture based on widely used VGGface16-Net for this problem. In this framework, the VGGface16-Net is used to jointly learn an common optimal nonlinear discriminative features from the non-frontal facial expression samples between the source and target databases and then we design a novel transductive transfer layer to deal with the cross-database non-frontal facial expression classification task. In order to validate the performance of the proposed transductive deep transfer learning networks, we present extensive crossdatabase experiments on two famous available facial expression databases, namely the BU-3DEF and the Multi-PIE database. The final experimental results show that our transductive deep transfer network outperforms the state-of-the-art cross-database facial expression recognition methods.
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