Exact(2)
Table 1 Facial expression recognition rate using the CK+ dataset with different subspace dimensionalities (case 1: no.
Table 3 Facial expression recognition rate using the JAFFE dataset with different subspace dimensionalities (case 1: no.
Similar(58)
Regarding the evaluation of the influence of different parts on facial image to the expression recognition rates, we considered on both occlusion and unocclusion local regions.
According to a study that investigated facial expression recognition using LBP-TOP features, VS and near-infrared images produced similar facial expression recognition rates, provided that VS images had strong illumination [33].
Testing experiments Figure 5 indicates the expression-recognition rate for different feature detectors based on the proposed framework.
Figure 6 Expression-recognition rates based on different component selection.
Figure 5 Expression-recognition rates on different features.
Experiment results demonstrate that the proposed RDAB facial expression method achieves a high recognition rate and outperforms other facial expression recognition systems.
Xiaoli et al.[8] used a 28 geometrical feature set to recognize seven basic expressions and recorded a recognition rate of 90.2% using the PNN classifier.
Hao and Thomas used 96 lines and their slopes[9] to recognize six basic expressions and recorded a mean recognition rate of 87.1% using the SVM classifier.
Tekguc et al.[16] used the NSGA-II feature extraction technique to classify the seven facial expressions and reported an average recognition rate of 88.1% using the PNN classifier.
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