# CEPP: Perceiving the Emotional State of the User Based on Body Posture
## 4 Performance Evaluation
### Evaluation with Respect to Computation Time  
As the last part of this experiment, we acquired the computation time for extracting features and computed the similarity distance, \(\zeta\), for all of the scenes. Figure 7 represents the result of computational time for conducting feature extraction and similarity distance, where the \(x\)-axis illustrates the index of the video frame and the \(y\)-axis depicts the computation time. Within the figure, we acquired the computation time for computing the overall similarity distance for one input image and the ground truth image. Moreover, we obtained the average time for computing one similarity distance. The same procedure is applied to extract the features. As we can see in the figure, recognizing one emotional state for one image cost less than 1 second. For the video scenes of: (1) all of the emotions; (2) positive emotions; (3) negative emotions; and (4) specific emotions, the computation time for recognizing the emotional condition were 827.08 ms, 911.36 ms, 749.61 ms, and 807.76 ms, respectively. For computing single similarity distance the average computation times were 137.84 ms, 151.9 ms, 124.94 ms, and 134.63 ms for four video scenes. This refers to computing the similarity distance between the input image and only one ground truth image. The computation time for feature extraction did not change much compared to computation of the similarity distance. As we can see in the figure, feature extraction was not heavily affected, even though the user posed in different body postures. However, computing similarity distance was different. Different body posture affected the computation time. If the user modifies his body posture, the computation time for similarity distance is affected. We believe that different body posture changed the features of the user's body and affected the computation time. The summary of the computation time of the proposed algorithm is listed in Table 4.  
Figure 6: Accuracy rate of the overall experiment.  
Conclusively, we could verify that extracting the feature within _CEPP_ cost more than computing the similarity distance. Moreover, for analyzing one video frame with all of the ground truth images, including feature extraction and computing similarity distance, it took less than 1 s. Overall, _CEPP_ is light and requires less computational resources for its operation. We observed that similar result is shown if _CEPP_ is implemented in the mobile device, since the hardware specification for our testing machine and the mobile device is similar.  
All in all, we can verify that our proposed algorithm's computational time is less than 1 second. However, compared with the other algorithms that were proposed to identify the emotional state of the user, our algorithm is more robust. One of the motives for this reasoning is that the other algorithms are based on the machine learning algorithm such as the Bayesian classifier and the support vector machine. Machine learning algorithms require extra computational time and yet more time for training of the data. Nevertheless, our algorithm does not need such computational complexity to recognize emotions.