Recognition method of unspecified face expressions based on machine learning

Traditional face recognition methods usually complete facial expression recognition for designated faces, and the pixel set at the edge of face image is chaotic, which leads to poor accuracy of unspecified facial expression recognition. In order to improve the accuracy of unknown facial expression recognition, a method of unknown facial expression recognition based on machine learning is proposed. The feature detection model of unspecified facial expressions is constructed, and the features are divided into regional blocks. Fusion block feature information establishes a spatial feature projection model, weights the feature information entropy, extracts statistical features and edge information entropy features, reorganises features and matching edge pixel sets and completes the recognition of various facial expression features. Experimental results show that the accuracy of this method is significant, reaching 1, which effectively improves the recognition efficiency and anti-interference performance.