Lightweight Facial Attractiveness Prediction Using Dual Label Distribution

arXiv:2212.01742v2 Announce Type: replace
Abstract: Facial attractiveness prediction (FAP) aims to assess facial attractiveness automatically based on human aesthetic perception. Previous methods using deep convolutional neural networks have improved the performance, but their large-scale models have led to a deficiency in flexibility. In addition, most methods fail to take full advantage of the dataset. In this paper, we present a novel end-to-end FAP approach that integrates dual label distribution and lightweight design. The manual ratings, attractiveness score, and standard deviation …

arxiv cs.cv distribution prediction type