Effective Attention-Based Feature Decomposition for Cross-Age Face Recognition

Round 1

Reviewer 1 Report

All observations and comments of the reviewer are highlighted on the attached file.

Comments for author File: Comments.pdf

Author Response

We thank you and anonymous reviewers for your constructive comments. We have done our best to fully address your suggestions in the revised manuscript. Our reply is attached. Please see the attachment.

Author Response File: Author Response.docx

Reviewer 2 Report

Decision: Major Revision

1. The “Abstract” section can be made much more impressive by highlighting your contributions. Add the numerical values in the abstract against state-of-the-art methods.
2. In the first contribution: explain how you have reduced the complexity and what kind of parameter you have reduced for that.
3. In the second contribution: explain the invariant feature and sum loss function; how they identify the age-related features. I didn’t find any info about it and I wonder.
4. In the last contribution: add numerical value, how much you have increased the accuracy and reduced the model complexity.
5. Introduction Section: The current challenges are not crystal clearly mentioned/very limited in the introduction section. Need more attention to the current challenges in this area followed by the authors’ contribution to overcoming those challenges.
6. In addition, the authors must provide a sufficient critical review of the literature to indicate the drawbacks of existing approaches and then define the main focus of the research direction. How did those previous studies perform? Readers need more positive reviews of the literature to indicate the state-of-the-art development.
7. In Section 3, the main para the authors must introduce their proposed research framework more effectively. For example, the authors could consider some essential brief explanations compared to the text with a total research flowchart or framework diagram for each proposed algorithm to indicate how these employed systems are working to receive the experimental results. It is not easy to understand how the proposed approaches work.
8. The manuscript, however, does not link well with recent literature on recognition that appeared in relevant top-tier journals, e.g., the IEEE Intelligent Systems department on " att-net: Enhanced emotion recognition system using lightweight self-attention module". Also, new trends of AI for recognition “mlt-dnet: recognition using 1D dilated CNN based on multi-learning trick approach” are missing it should be comprised.
9. The readability and presentation of the study should be further improved. The paper suffers from language problems.
10. Section Conclusion - Authors are suggested to include in the conclusion section the real actual results for the best performance of their proposed methods in comparison towards other methods to highlight and justify the advantages of their proposed methods with possible future direction.
11. What is the main difficulty when applying the proposed method? The authors should clearly state the limitations of the proposed method in practical applications and should be mentioned in the article's conclusion.

Author Response

We thank you and anonymous reviewers for your constructive comments. We have done our best to fully address your suggestions in the revised manuscript. Our reply is attached.   Please see the attachment.

Author Response File: Author Response.docx

Round 2

Reviewer 2 Report

the authors have successfully addressed my suggestions and comments!