Makeup Product Image
Makeup product image analysis is a burgeoning field focusing on automated extraction of product attributes and their application in various contexts, such as personalized recommendations and virtual try-on experiences. Current research emphasizes developing robust machine learning models, including generative adversarial networks (GANs) and diffusion models, to analyze images, extract features like color and texture, and even generate realistic makeup effects. This work is significant for improving e-commerce experiences, enhancing facial privacy protection through adversarial techniques, and advancing computer vision capabilities in handling complex visual data like makeup.
Papers
Molecular Dynamics and Machine Learning Unlock Possibilities in Beauty Design -- A Perspective
Yuzhi Xu, Haowei Ni, Qinhui Gao, Chia-Hua Chang, Yanran Huo, Fanyu Zhao, Shiyu Hu, Wei Xia, Yike Zhang, Radu Grovu, Min He, John. Z. H. Zhang, Yuanqing Wang
Deep neural network-based detection of counterfeit products from smartphone images
Hugo Garcia-Cotte, Dorra Mellouli, Abdul Rehman, Li Wang, David G. Stork