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OpenCV 3 Blueprints by Utkarsh Sinha, Quan Hua, Steven Puttemans, Joseph Howse

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Evaluation

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 ...

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