Open AccessAn ensemble approach for art face recognition problem
Author(s) -
Cunzheng Wang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1848/1/012067
Subject(s) - convolutional neural network , computer science , artificial intelligence , face (sociological concept) , pattern recognition (psychology) , feature (linguistics) , ensemble learning , facial recognition system , support vector machine , ensemble forecasting , ideal (ethics) , machine learning , set (abstract data type) , social science , linguistics , philosophy , epistemology , sociology , programming language
In recent years, with the development of the digitalization of cultural relics, many cultural relic restoration models based on different calculation methods have been proposed. Although the models for feature classification (such as VGG16, Xception) or ensemble models (such as SVM and ANN) are relatively mature, multiple classification problems and accuracy problems are still far from ideal. This paper proposes an ensemble method for artistic face recognition based on multiple models. Six convolutional neural network models are used as basic models to compare their performance in gender and status classification. Based on the KaoKore data set, after training them to obtain the corresponding characteristics, the ensemble model is used for weight combination to obtain a more accurate model. It has achieved 98.10% and 90.13% accuracy on gender and status classification respectively, which is the best in the current literature.