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Cross-dataset face analysis based on multi-task learning
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DOI：10.1007/s10489-022-03173-4.png)
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Facial attributes are fundamental for studying deep structured information. Single-task face analysis reaches great performance, while analysis of multiple attributes meets challenges, including the network design and cross-dataset learning. In this paper, we propose cross-dataset face analysis based on multi-task learning (CFA-Net), which accomplishes landmark, head pose, age, gender, facial expression, and Action Unit (AU) analysis. Firstly, we balance between the shared and the task-specific structure to design an efficient and accurate network. To guarantee the excellent performance of each task, we utilize classification-based, regression-based, ranking-based, or deep label distribution learning-based methods to extract specific features for diverse tasks. Then, face analysis trained on a single dataset has strict requirements for this dataset. Even if this dataset currently meets the demand, the scalability is poor when tasks increase. Therefore, our training set is a mixture of multiple datasets, and each dataset covers one or several task related labels. Each sample possesses one or several tasks' labels, and we adopt a sample-dependent loss strategy, which only penalizes available ground truth. The proposed CFA-Net only occupies 1.58G GPU memory and costs 0.021s to address one image. In summary, the proposed CFA-Net behaves fast, occupies less memory, and performs well in every subtask, even better than those under single-task training.
Keyword:
Multi-task learning
Cross-dataset training
Face analysis
AU detection
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BP4D-Spontaneous: a high-resolution spontaneous 3D dynamic facial expression databaseBP4D-Spontaneous: 高分辨率自发三维动态面部表情数据库
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