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I am pleased to confirm that you have successfully addressed all of the reviewers' comments in your revised manuscript. Although the previous reviewers were not re-invited for this round of assessment, I have thoroughly evaluated the revisions myself. I am satisfied with the current version and believe it meets the standards for publication
[# PeerJ Staff Note - this decision was reviewed and approved by Xiangjie Kong, a PeerJ Section Editor covering this Section #]
Thank you for submitting your manuscript titled “Emerging trends in gait recognition based on deep learning: A survey” to PeerJ Computer Science. Based on the reviewers' feedback, minor revisions are required before publication.
The reviewers highlighted several areas for improvement. They suggested enhancing the use of connectors and improving the structure of the introduction. Proper integration and introduction of referenced works are necessary to increase readability and coherence. Additionally, the manuscript should adhere to the journal's formatting guidelines, including correct fonts for sections and subsections. While the conclusions are sound and the bibliographic review is comprehensive, minor refinements in language and presentation are needed. Furthermore, a direct comparison with the works mentioned by the reviewers should be included within the manuscript, rather than only in the response to reviewers.
Please address these points in your revised manuscript and submit it along with a detailed response letter outlining the changes made within.
Best regards,
[# PeerJ Staff Note: The review process has identified that the English language should be improved. PeerJ can provide language editing services if you wish - please contact us at [email protected] for pricing (be sure to provide your manuscript number and title). Your revision deadline is always extended while you undergo language editing. #]
Regarding the level of English, they should use a greater number of connectors and correctly introduce or give way to the referenced works (included in the comments), in order to increase the level.
The introduction should be improved and properly structured (included in the comments).
The subject matter used is very interesting and is in line with the technological scope of the journal.
As mentioned above, they should correctly introduce or give way to the referenced works (including in the comments).
The information is organized in sections and sub-sections, which facilitates reading. Even so, some improvements are detailed in the comments.
The conclusions are correct. The bibliographic review of this scientific work serves as a guide to knowledge about the different methodologies used and applied in this area in recent years.
I consider that the changes carried out give a qualitative leap to the scientific article, but they are not enough. Therefore, in the review, I set out the various improvements to be made, in order to further complete the study and improve its readability.
I believe the work is better now, but small changes are still needed.
The authors should pay attention to the journal format, I do not think it is in the journal's format. Please correct the fonts for the sections and subsections.
The work is very consistent, I believe it may be published after some minor changes.
The comparison between this work with the others I mentioned must be in the text to be published, please elaborate this comparison in the work, not only in the response to reviewers. This is the main reason I suggest minor reviews to this work.
Thank you for submitting your manuscript titled "Emerging Trends in Gait Recognition Based on Deep Learning: A Survey" to PeerJ Computer Science. We have received the reviews of your manuscript, and after careful consideration, we have decided that your manuscript can be reconsidered for publication after major revisions.
The reviewers have recognized the relevance of your survey on deep learning techniques in gait recognition and its alignment with the scope of our journal. However, they have identified several areas that require enhancement to meet our publication standards. These include the need for a clearer differentiation of your work from existing literature, particularly recent surveys in the field. Both reviewers have emphasized the importance of refining your content's organization and presentation, including the introduction's structure, the formatting of figures and tables, and the overall use of English to ensure clarity and coherence.
Furthermore, there is a consensus that the discussion on the state-of-the-art methodologies could be more comprehensive, providing deeper insights into the strengths and gaps of current research. Additional references and a more critical analysis of recent developments are suggested to bolster your survey's contribution to the field.
Please address these points comprehensively in your revision and resubmit your manuscript. We look forward to receiving your revised submission and hope that you will find the reviewers' insights helpful in enhancing your manuscript.
**PeerJ Staff Note:** It is PeerJ policy that additional references suggested during the peer-review process should only be included if the authors agree that they are relevant and useful.
**PeerJ Staff Note:** Please ensure that all review, editorial, and staff comments are addressed in a response letter and that any edits or clarifications mentioned in the letter are also inserted into the revised manuscript where appropriate.
**Language Note:** The review process has identified that the English language must be improved. PeerJ can provide language editing services - please contact us at [email protected] for pricing (be sure to provide your manuscript number and title). Alternatively, you should make your own arrangements to improve the language quality and provide details in your response letter. – PeerJ Staff
The scientific study has sufficient bibliographic references, although the attached comments detail the inclusion of some more.
The figures provide information, although some visual formatting should be improved (included in the comments).
The tables provide information, although the organization of the information should be improved in some tables (included in the comments).
Regarding the level of English, they should use a greater number of connectors and correctly introduce or give way to the referenced works (included in the comments), in order to increase the level.
The introduction should be improved and properly structured (included in the comments).
The subject matter used is very interesting and is in line with the technological scope of the journal.
It has been observed that the information on the different methodologies detailed is sufficient, objective and technical, being directed towards an expert public in the subject.
As mentioned above, they should correctly introduce or give way to the referenced works (including in the comments).
The information is organized in sections and sub-sections, which facilitates reading. Even so, some improvements are detailed in the comments.
As this is a bibliographic review, there is no quantitative or technical-scientific reproducibility.
The conclusions are correct, but it remains to answer several questions (inserted in the comments) in order to respond to the established objectives and results obtained (important).
The bibliographic review of this scientific work serves as a guide to knowledge about the different methodologies used and applied in this area in recent years.
I believe that the following scientific work addresses a very interesting topic in reference to the different methods of biometric identification in gait recognition. In addition, it is possible to observe its different uses, associated challenges, and the importance of deep neural networks in this matter. Even so, several improvements are considered, in order to further complete the study and improve its readability, and, therefore, to make a qualitative leap according to the works published in this journal.
This survey is about Deep Learning techniques applied to Gait recognition problem. 19 works were deeply analysed, with the inclusion of the description of the most important datasets in this field.
There are some mistakes in the English but I considered as minor problems.
The overall structure looks good, although I miss some organization, such as suggested in the last paragraph of "Introduction" section (ex: 2. Fundamentals, 3. Different approaches... which is not in the correct font I believe) . I am not aware if it is a problem on the document visualization or if it was design in this way.
In my opinion, this work belongs to the scope of the journal, however, it is not clear the difference between this work and other recent developed works in the field, I suggest the author to develop this explanation. Here is a list of works that should (but not limited to) be compared to:
Gait Recognition Based on Deep Learning: A Survey https://dl.acm.org/doi/full/10.1145/3490235
Deep Gait Recognition: A Survey: https://ieeexplore.ieee.org/abstract/document/9714177?casa_token=dW2LobBow7AAAAAA:ljQTHXNF-PuKIdPkDu_E0rA7WTH4TC7s9Lt8kbO2p73BbNkU79G5NguCEBC8mfGiv4pnmtkEMx0
What is the difference of the current work from these others? What points are discussed that are not discussed in the others? What are the strongest points? What are the weakest?
The methodology provided looks consistent to me, I really liked it.
The work looks good but I believe the authors should pay attention to the fonts used in the text. For example: Section 3 is not using bold or bigger size font, it is in the same size as all the normal text.
Here is a list of general problems in the text in my opinion:
Introduction:
I suggest Table 3 to be used here instead of the current section it is used.
Fundamentals:
2. Usefulness of Gait Recognition Technology:
I believe all this section might be rewritten because there is just one paragraph in each one. I suggest the authors go deeper in each sub-section, providing more information for each case.
General Steps for Human Gait Recognition System using Deep Learning Techniques
This section is missing a lot of references, such as the ones to architectures (Mask R-CNN, ResNet…) and for the losses (triplet loss, identity similarity loss…)
Literature review on human activity recognition
Section 1.2 has some errors, such as: remove "In", "Conventional" instead of "Convolution" and so on. I suggest this paragraph to be rewritten.
Experimental Results & Analysis
Second paragraph: remove the word "The" (should be "Table 6 provides…")
I believe the bigger gap in this text is the missing of a more consistent discussion: what is the state-of-the-art (SOTA) method for gait recognition they found? Why such work is considered SOTA? What are the common gaps in the works they analyzed? What researchers should be looking for when developing new methods for gait recognition? I strongly suggest a new Section to discuss all these (and other) points.
I miss studies using Transformers and Diffusion Models, is there anything developed using these methods? Why are they not included?
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