Live feature transformation scale extraction of face images based on deep learning algorithm

In view of the low extraction accuracy and efficiency of the current feature extraction methods of face images, a method for extracting the scale of live feature transformation of face images based on deep learning algorithm is proposed. First, the deep learning method is used to complete the denoising of face images; then the Gabor wave filter is used to decompose the face signal, and it is input into the deep subspace model to complete the extraction of feature transformation scale; finally, the live feature transformation scale extraction of face images is completed based on (particle PSO swarm optimization algorithm). Experimental results show that the proposed method for extracting face image features has higher accuracy, faster recognition speed and better overall application effect.