Face Attribute Recognition Based on Graph Convolutional Neural Networks
The research on multi-attribute recognition of facial images and the modeling of dependencies between multiple labels is a highly concerned research topic in the fields of Computer Vision and Machine Learning.A multi-label facial attribute recognition model based on Graph Convolutional Neural Networks is proposed to improve recognition efficiency by leveraging the dependency relationships between multiple labels.This model constructs a directed graph between facial attributes in a data-driven manner,and maps each attribute to the corresponding attribute classifier using a Graph Convolutional Neural Networks to model the dependency relationships between categories.The model has conducted in-depth analysis on key elements such as correlation matrix and feature matrix in Graph Convolutional Neural Networks,enabling it to handle multi-label facial attribute recognition problems.The experimental results show that the model performs well on the authoritative dataset CelebA for multi-label facial attribute recognition and can maintain a meaningful semantic structure.
Deep Learningface attribute recognitionGraph Convolutional Neural Networksmulti-label classification
国家科技期刊平台
NSTL
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