Study on athlete's facial emotion recognition based on Kalman filter Online publication date: Mon, 31-Oct-2022
by Nianhui Wang; Qingxue Li; Yuanhua Li
International Journal of Reasoning-based Intelligent Systems (IJRIS), Vol. 14, No. 4, 2022
Abstract: Aiming at the problems of high loss rate of expression details, poor comprehensiveness of recognition results and low recognition rate in traditional methods, an athlete's facial emotion recognition based on Kalman filter is proposed. Firstly, the uncertainty of athlete's facial emotion image is described according to the active learning algorithm. Then, with full consideration of the uncertainty factors, the feature block method is used to mosaic the image. Finally, according to the splicing results, a first-order motion model is established by Kalman filter to track and calibrate the image target points to complete the facial emotion recognition. The results show that the expression detail loss rate of this method is low, the comprehensiveness coefficient of recognition results is high, and the emotion recognition rate is always higher than 90%, indicating that the recognition effect of this method is better.
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