A CNN-Based Visual Framework for Peking Opera Facial Mask Recognition with Multi-Feature Cultural Representation

Abstract

Peking Opera facial masks constitute a highly symbolic visual system in traditional Chinese performing arts, where color and structural patterns encode rich cultural meanings such as character identity and moral traits. In recent years, advances in compute r vision and deep learning have enabled increasing interest in applying automated methods to the recognition and analysis of such culturally significant visual forms. This paper suggests the usage of a convolutional neural network (CNN)-based system to identify Peking Opera face masks through a combination of color, texture, structural symmetry, features. The framework attempts to overcome the shortcomings of generic models of deep learning in the ability to capture culturally based visual semantics. The model is created on a ResNet backbone and the transfer learning approach, which is aimed at small-sample cultural data. Experimental observations of related works indicate that the use of domain specific representations enhances classification accuracy and minimizes color bias that is evident in CNN models. The results show that CNNs are useful in visual pattern recognition, but they have a poor capability of deciphering cultural meaning. The paper emphasizes the need to integrate computing techniques with cultural priors to augment cultural visual recognition tasks in their interpretability and strength.