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The authors have tried to answer most of the questions. The name and surname of some references are not correct. Please check them.
no comment
no comment
no comment
This reviewer would like to thank the authors for their effort in clarifying and modifying this paper. I have no other questions. I suggest that the paper is accepted for publication.
This paper needs further revision.
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1. The writing of the paper (e.g. grammar, tense) should be improved to make it more professional.
2. Some references on related works including gait recognition and quality assessment are suggested to be added for sufficient field background.
[1] Xianye Ben, Peng Zhang, Zhihui Lai, Rui Yan, Xinliang Zhai, Weixiao Meng. A general tensor representation framework for cross-view gait recognition, Pattern Recognition, vol. 90, pp. 87-98, 2019.
[2] Lei Chen and Jiying Zhao, No-reference perceptual quality assessment of stereoscopic images based on binocular visual characteristics, Signal Processing: Image Communication, vol. 76, pp. 1-10, 2019.
3. The format of references should be indentical, e.g. Line 332 and Line 340.
Many important details are missing in the experimental design. For instance, how many dataset (or proportion) is used for training or testing? Some parameters of Adam optimizer are also not provided.
1. For the compared methods, Are the results obtained from the corresponding papers or conducted in your experiments, which need to be clarified.
2. More explanations or analysis should be added for performance comparion, instead of just listing the results.
The paper proposed an efficient video face recognition based on frame selection and
quality assessment. There are several issues to be addressed based on the comments.
Your method needs more detail. I suggest that you improve the description of the proposed method to your research more clear to readers
Experiment is Ok.
This article has certain innovation.
The authors train the lightweight CNN (“FaceQNet mobile”) for face quality analysis by distilling the knowledge of the FaceQNet ResNet-50 model. The reported experiments demonstrate the performance of the proposed method. My comments are as follows:
1. The innovation of this paper is not clear and it is difficult for readers to understand the main contributions of this paper. This part should be added in Introduction section.
2. In the proposed framework (Figure 3), which part is the method proposed by the authors?
3. In Figure 1, what is teacher model? and what is Student model?
4. Some details of the proposed method are not clearly described in the paper.
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