A portrait image recommendation method based on collaborative filtering
Yuwei Hu, Xueyuan Zheng, Ping Zong · 2023
Portrait recognition is a key task in public security area. Most present portrait recognition methods lay more emphasis on extracting facial features by different algorithms for a variety of scenarios, but focus less on fusion of multiple recognition results. In this paper, we propose a portrait image recommendation method based on collaborative filtering, for which the main task is to improve the hit accuracy by utilizing the portrait recognition results from multiple feature-based models. Considering these models preferences and historical scores on portrait images, user-based collaborative filtering is applied to calculate the similarity between the models. Meanwhile, due to the difference of similarity criteria, an improved comprehensive similarity model based on Auto-Encoder is established to synthesize similarity values calculated by different feature-based models to predict the rating scores of the candidate portrait images. Experiments on portrait images in the wild show that the hit accuracy reaches 85.48% and is 1.11% higher than that best results of the single feature-based models, which illustrates that the proposed method is effective and practical for recommendation of portrait images.