In this section, we will show the performance of our facial expression recognition system. In our test, we will keep the parameters of each learning algorithm the same and only change the feature extraction. We will evaluate the feature extraction with the number of clusters equaling 200, 500, 1,000, 1,500, 2,000, and 3,000.
The following table shows the accuracy of the system with the number of clusters equaling 200, 500, 1,000, 1,500, 2,000, and 3,000.
Table 1: The accuracy (%) of the system with 1,000 clusters
|
K = 1000 |
MLP |
SVM |
KNN |
Normal Bayes |
|---|---|---|---|---|
|
SIFT |
72.7273 |
93.1818 |
81.8182 |
88.6364 |
|
SURF |
61.3636 |
79.5455 |
72.7273 |
79.5455 |
|
BRISK |
61.3636 |
65.9091 |
59.0909 |
68.1818 |
|
KAZE |
50 |
79.5455 |
61.3636 |
77.2727 |
|
DAISY |
59.0909 |
77.2727 |
65.9091 |
81.8182 |
|
DENSE-SIFT |
20.4545 |
45.4545 |
43.1818 |
40.9091 |
Table 2: The accuracy (%) of the system with 500 clusters
|
K = 500 |
MLP |
SVM |
KNN |
Normal Bayes |
|---|---|---|---|---|
|
SIFT |
56.8182 |
70.4545 |
75 |
77.2727 |
|
SURF |
54.5455 |
63.6364 |
68.1818 |
79.5455 |
|
BRISK ... |