Cartoon character recognition based on portrait style fusion

In this paper, we propose a cartoon character recognition method using portrait characteristics to address the problem of copyright protection in cartoon works. The proposed recognition framework is derived from content-based retrieval mechanism, achieving an effective solution for copyright identification of cartoon characters. This research has two core contributions. The first is that we propose an ECA-based residual attention module to improve cartoon character feature learning ability. Cartoon character images typically have fewer details and texture information, and inter-channel information interaction can more effectively extract cartoon features. The second is a style transfer-based cartoon character construction mechanism, which is proposed to create a simulated plagiarized cartoon character dataset by fusing portrait style and content. Comparative experiments demonstrate that the proposed model effectively improves detection accuracy. Finally, we validate the effectiveness and feasibility of the model by retrieving plagiarized versions of cartoon characters.