A Deep Learning-Based Aesthetic Surgery Recommendation System

We propose in this chapter a deep learning-based recommendation system for aesthetic surgery, composing of a mobile application and a deep learning model. The deep learning model built based on the dataset of before- and after-surgery facial images can estimate the probability of the perfection of some parts of a face. In this study, we focus on the most two popular treatments: rejuvenation treatment and eye double-fold surgery. It is assumed that the outcomes of our history surgeries are perfect. Firstly a convolutional autoencoder is trained by eye images before and after surgery captured from various angles. The trained encoder is utilized to extract learned generic eye features. Secondly, the encoder is further trained by pairs of image samples, captured before and after surgery, to predict the probability of perfection, so-called perfection score. Based on this score, the system would suggest whether some sorts of specific aesthetic surgeries should be performed. We preliminarily achieve 88.9 and 93.1% accuracy on rejuvenation treatment and eye double-fold surgery, respectively

Facial Beauty Prediction and Analysis based on Deep Convolutional Neural Network: A Review

Abstract: Facial attractiveness or facial beauty prediction (FBP) is a current study that has several potential usages. It is a key difficulty area in the computer vision domain because of the few public databases related to FBP and its experimental trials on the minor-scale database. Moreover, the evaluation of facial beauty is personalized in nature, with people having personalized favor of beauty. Deep learning techniques have displayed a significant ability in terms of analysis and feature representation. The previous studies focussed on scattered portions of facial beauty with fewer comparisons between diverse techniques. Thus, this article reviewed the recent research on computer prediction and analysis of face beauty based on deep convolution neural network DCNN. Furthermore, the provided possible lines of research and challenges in this article can help researchers in advancing the state – of- art in future work