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This paper has been well addressed the two reviewers' questions. This paper can be accepted now.
[# PeerJ Staff Note - this decision was reviewed and approved by Paula Soares, a PeerJ Section Editor covering this Section #]
no comment
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All of my concerns have been addressed. This version may be considered to be accepted.
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Some experiments should be added in this paper, therefore, this paper needs careful revisions.
[# PeerJ Staff Note: It is PeerJ policy that additional references suggested during the peer-review process should only be included if the authors are in agreement that they are relevant and useful #]
[# PeerJ Staff Note: Please ensure that all review comments are addressed in a rebuttal letter and any edits or clarifications mentioned in the letter are also inserted into the revised manuscript where appropriate. It is a common mistake to address reviewer questions in the rebuttal letter but not in the revised manuscript. If a reviewer raised a question then your readers will probably have the same question so you should ensure that the manuscript can stand alone without the rebuttal letter. Directions on how to prepare a rebuttal letter can be found at: https://peerj.com/benefits/academic-rebuttal-letters/ #]
Fails in 'Professional article structure, figures, tables. Raw data shared'.
All the figures and tables are not embedded in the manuscript.
So this manuscript is very hard to read and understand.
no comment
Fails in 'All underlying data have been provided; they are robust, statistically sound, & controlled'.
The proposed method was only compard with SOTA methods on the Grimace dataset.
Compared with other SOTA methods, how about the performance on the Faces 95 and Faces 96 datasets?
All the figures and tables are not embedded in the manuscript.
So this manuscript is very hard to read and understand.
The proposed method was only compard with SOTA methods on the Grimace dataset.
Compared with other SOTA methods, how about the performance on the Faces 95 and Faces 96 datasets?
The English language using is a little poor, and this paper t is very hard to read and understand. The authors may miss the related latest work, such as: Video-based Facial Micro-Expression Analysis: A Survey of Datasets, Features and Algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence, DOI: 10.1109/TPAMI.2021.3067464
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The authors may miss the compared results on the Faces 95 and Faces 96 datasets.
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