Our proposed approach can be applied to other AUs as well. In Tab.6, LP-SM also considers apex frames on CK+, and The comparison to LP-SM is consistent. In Tab.8, we apply FMPN-FER and DeepEmotion to our pre-processed We will consider a pre-trained VGGFace model in our further work. R2 2.1 The novelty compared to prior work. Facial expression can be a group of AUs.

We thank all the reviewers for their valuable suggestions. Our response to individual reviewers' concerns are as follows. The scope of the two papers is different. The usage of AU relationship is different. The performance of our semi-supervised learning method is lower than [1] on BP4D.

We adapt the generic knowledge from existing studies that are applicable to different datasets. Generic knowledge is expressed as probabilities. The generic knowledge is categorized into three types: expression-dependent single AU probabilities, expression-dependent joint AU probabilities, and expression-independent joint AU probabilities. 1) For expression-dependent single AU probabilities, two sources are considered. According to FACS, given an expression, AUs can be grouped into primary (P) and secondary (S) categories. The primary AUs are the most expressive AUs with respective to the expression, and the secondary AUs may co-occur with primary AUs providing additional supports for the expression.

Our proposed approach can be applied to other AUs as well. In Tab.6, LP-SM also considers apex frames on CK+, and The comparison to LP-SM is consistent. In Tab.8, we apply FMPN-FER and DeepEmotion to our pre-processed We will consider a pre-trained VGGFace model in our further work. R2 2.1 The novelty compared to prior work. Facial expression can be a group of AUs.

Additional Feedback: The work is a good incremental step towards understanding the relationship of AU and FER, and their influence in detecting one over the other. Figure 1: I am assuming that the dotted lines represent back-propagation steps for each module. Please clarify this in the manuscript/Figure. Sec 3.1: The explanation on using the generic knowledge as probabilities is not unique ([b]), and the usage of limited 8 AUs (there are a lot more) is not justified. While generating Table 1, it is important to note that these numbers are taken from studies which explored more AUs than mentioned in the table.