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The block diagram of the automatic facial expression recognition system is shown in Figure 1.
In this study, we propose a message based facial expression recognition system using geometrical features.
This study improves the recognition accuracy and execution time of facial expression recognition system.
An automatic facial expression recognition system generally comprises of three crucial steps [2]: face acquisition, facial feature extraction, and facial expression classification.
Experimental results show that the performance of the proposed filter achieved excellent average recognition rates, when it is applied to facial expression recognition system.
In the last step of an automatic facial expression recognition system, i.e., facial expression classification, a classifier is employed to identify different expressions based on the extracted facial features.
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In general, facial expression recognition systems are designed considering the visible spectrum.
Experiment results demonstrate that the proposed RDAB facial expression method achieves a high recognition rate and outperforms other facial expression recognition systems.
Most of the facial expression recognition systems attempt to recognize a set of expressions like anger, disgust, fear, joy, sadness, and surprise.
In this paper, we have designed an facial expressions recognition system based on sEMG signals using Intel Edison board with advantages of high temporal resolution, potential flexibility of testing devices.
However, in some uncontrolled conditions, including varying illumination, poses, facial expressions, and noise, the performance of face recognition system would be dramatically affected.
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