Miner expression recognition based on improved principal component analysis

Abstract

Aiming at the problem that the feature extraction of miners' facial expressions is slower and the recognition accuracy is not high for the traditional miner facial expression recognition method, based on the principal component analysis method, Fisher's linear discriminant method is used to improve the traditional principal component analysis method. Firstly, based on the principal component analysis method, an inter-class discrete matrix is added to make the distance between the feature points of different categories become larger after projection, and the distance between the feature points of the same category is more compact, so that the result of feature extraction to the miners' facial expression images is more representative and targeted. Then, the radial basis network is used to map the low-dimensional and nonlinear separable miner's facial expression feature matrix to the high-dimensional spatially separable class to realize the identification and classification of miners' facial expressions. The experimental results show that the recognition rate of the miner's facial expression reaches 89.0%, which is superior to the traditional miner's facial expression recognition algorithms. The method has a good application prospect in the fields of mine safety monitoring and fatigue driving.

Keywords

• computer image processing
• miner expression recognition
• principal component analysis
• Fisher linear discriminant
• radial basis network