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Responses Reviewer #1 (1)

Manuscript Number: EVOS-D-20-00113
Article Title: A novel approach for facial expression recognition based on
genetic algorithm
Journal: Evolving Systems
Authors: A. Boughida, M.N. Kouahla, Y. Lafifi
Response to reviewer # 1
We appreciate the reviewer’s helpful comments and wish to thank him for the
time spent on our paper.
Reviewer’s comment #1
English expressions need polishing. For instance, "The stop stopping chosen is when the number of
generations…" does not make sense to me.
Author’s answer
We have considered these remarks; a native English speaker checked the grammatical review.
For instance, “The stop stopping”, becomes “The stop criteria”.
Reviewer’s comment #2
P12L33 "The mutation rate is usually chosen to be 1/m [], where m is the length of the
chromosome." I think that it misses a citation.
Author’s answer
Yes, we just forgot to insert the citation that refers to the mutation rate that I used in the genetic
algorithm. The text becomes after insertion of the citation: "The mutation rate is usually chosen to
be 1/m [52], where m is the length of the chromosome."
Where the added reference is: [52]. Ghodrat Moghadampour. Outperforming mutation operator
with random building block operator in genetic algorithms. In International Conference on Enterprise
Information Systems, pages 178–192. Springer, 2011.
Reviewer’s comment #3
In 5.1 the authors state that they performed a random train-test split with 0.75/0.25. Since the
samples are below 1000 why did not perform cross-validation instead of keeping a hold-out dataset?
Author’s answer
Most of the related works use directly the cross-validation strategy with the CK, CK +, and JAFFE
databases. That's why we redid the whole experiment with 10-fold-cross-validation. Despite this,
there is work that uses the hold-out strategy in experimenting with the same databases as work
(Yang, et al, 2010), where the authors have randomly divided the data into 66 % train set and 33%
test set. We therefore decide to preserve some results with the Hold-out strategy (Results of table 6
become table 9).
PS: Since the use of 10-fold-cross-validation caused a redo of the whole experiment, values were
changed: The population size becomes 200 instead of 80 (see 4th experiment). Also, we were forced
to set the maximum iterations of the genetic algorithm by 200 instead of 100 because the algorithm
does not converge at 100 generations with this new experiment.
The downside when using 10-fold-cross-validation is the high complexity compared to Hold-out,
because for each chromosome the learning will be repeated 10 times. We notice that the results are
very close to that of hold-out, with a non-decisive advantage.
Modification of paragraph: section 5.2, page 14.
Addition of paragraph: section 5.2, 2nd experiment, page 17
Reviewer’s comment #4
Using a single number, we cannot conclude for the significance of the results of Table 6.
Author’s answer
To conclude the significance of the results of Table 6 (renamed to table 7), we can compare the
results of GA-based method and Randoized search based method by using not only the recognition
rate but also the number of features used (a strong point of our contribution). For this, we have
added a table 9 (new table) which compares the size of the feature vector of the two methods. For
the method based on Randomized Search, the size of feature vector is directly the number of
features after reduction with PCA unlike to our method, which will optimize the feature vector.
Modification of paragraph: section 5.2, 2nd experiment, page 16.
Addition of table 8: section 5.2, 2nd experiment, page 17
Reviewer’s comment #5
Did the authors perform stratification when splitting the dataset in train/test splits?
Author’s answer
Of course, we used it in k-fold-cross-validation, to preserve the same proportions of examples in each
class as seen in the original dataset.
Modification of paragraph: section 5.2, page 14.
Reviewer’s comment #6
What is the distribution of each expression in each dataset?
Author’s answer
The distribution for each expression in each dataset:
CK+: Angry (An) 45, Contempt (Co) 18, Disgust (Di) 59, Fear (Fe) 25, Happy (Ha) 69, Sadness (Sa) 28,
Surprise (Su) 83.
CK: Angry (An) 101, Disgust (Di) 20, Fear (Fe) 33, Happy (Ha) 112, Sadness (Sa) 150, Surprise (Su) 70.
JAFFE: Angry (An) 30, Disgust (Di) 29, Fear (Fe) 32, Happy (Ha) 31, Neutral (NE) 30, Sadness (Sa) 31,
Surprise (Su) 30.
We have added these distributions in table 4, page 13, section 5.1 that describes the
distribution for each expression in each of the three datasets: CK, CK +, and JAFFE.
Reviewer’s comment #7
The authors used everywhere the accuracy. Assuming that we are interested in all expressions,
accuracy makes sense if the distribution of the expressions is similar for each class, otherwise a
metric that takes into account the class imbalance should be employed.
Author’s answer
We can compare the recognition rates of the three databases by adding the F1-score metric, which
will allow us to take into account the imbalance problem.
We noticed that accuracy ≈ f1_score for the three databases (for example for CK+ f1_score =
accuracy = 94.20%). For that, the use of accuracy is not going to be a problem. Also, we decide to use
accuracy to compare our recognition rate with that of related work.
Addition of 2 paragraphs: section 5.2, page 14 and page 15.
Reviewer’s comment #8
In 5.2.1 in the first experiment the authors compare their proposed approach against the grid search
approach using predefined values. This is not adequate and on each own does not show the
superiority against their genetic algorithm. For fair comparison they should compare against the
random search approach where for same iterations (100) they should sample the hyper parameters
at random from a uniform distribution.
Author’s answer
We replaced GridSearch with Ramdomized Grid Search. It will allow the C/gamma pairs to be chosen
at random, and it stops in iteration 100. The pair chosen will be the pair which gives the best
PS: As we said above, we changed the max iteration by 200
Modification of paragraph: section 5.2, 2nd experiment, page 16.
Reviewer’s comment #9
I think that Table 5 is redundant.
Author’s answer
It is not too important in the comparison between our approach and Randomized Seacrh (we
replaced Grid Search with Randomized Search). It will just make it possible to compare each fold of
the GA-based method with that of the Randomized search. That is why we deleted it.
Deletion of table 5 page 15 (OLD PAPER)
Reviewer’s comment #10
Why did the authors not include the Gabor filter parameters inside their chromosome of their
genetic algorithm?
Author’s answer
We thought of enriching this genetic system with Gabor parameters in another separate work, but
several challenges are noted:
The codification of Gabor parameters in the chromosome
Increase in the complexity of the genetic system, because for each chromosome, we will not
only select the characteristics and learn the model, but also the extraction of the
The choice of the range of values for each of these parameters.
Addition of paragraph: section 6, page 20
------------------------------------------------------------------Dear Reviewer,
We have done our best to answer all your remarks and comments. We would like to thank
you for the time you spent reviewing our paper. We hope that, in our corrections, we have
followed your advice and done what you expected us to do.
Thank you for your help.