Gender-Cartoon: Image Cartoonization Method Based on Gender Classification

Qin Opera art is one of China’s intangible cultural heritage, and its influence is gradually declining. The cartoonization of Qin Opera is one of the feasible methods. However, current cartoonization methods suffer from the inability to classify and accurately cartoonize Qinqiang portraits by gender. Therefore, we propose Gender-Cartoon, which can achieve different gender portrait cartoons. The proposed method consists of four modules: gender classification, content feature extraction, gender identification style feature extraction and cartoonization. The gender classification module is used to obtain the gender labels of cartoon images, content feature module extracts content features from portraits by stacking convolutional blocks, and gender identification style extraction module uses the gender labels of cartoon images and image semantic features to obtain the corresponding gender style feature. The cartoonization module fuses content features and style features as the input of the adaptive residual block to obtain the corresponding cartoonization results. The experimental results show that the model can transform both texture and shape on the our collected Qinq Cartoon dataset Face2QinqCartoon and the public dataset Selfie2anime.