NDLI: Facial Beauty Prediction Based on Lighted Deep Convolution Neural Network with Feature Extraction Strengthened

Content Provider	IET Digital Library
Author	Gan, Junying Jiang, Kaiyong Tan, Haiying He, Guohui
Abstract	Convolutionneural network (CNN) has significantly pushed forward machine vision, which has achieved very significant results in face recognition, image classification and objection detection, and provides a new method for facial beauty prediction (FBP). Although the approach is widely applied in FBP, the research progress in FBP is relatively slow compared with face recognition. The first one is that there is less public database for FBP, and experiments for FBP are tested on small-scale database. The second one is that evaluation of facial beauty is subjective and lack of criterion,and CNN model is hard to train. In view of the problems of FBP, we expand Largescale database of Asian women's face database (LSAFBD) with data augmentation. A lighted deep convolution neural network (LDCNN) for FBP including 5650K parameters is constructed by both Inception model of GoogleNet and Max-Feature-Max activation layer, which can extract multi-scale features of an image, get compacted presentation and reduce parameters. Experiments on LSAFBD show that our LDCNN model has advantages of simple structure, small-scale parameters and is suitable for small embedded devices, with the best classification accuracy of 63.5%, which outperforms the other published CNN models for FBP.
Starting Page	312
Ending Page	321
Page Count	10
ISSN	10224653
Volume Number	29
e-ISSN	20755597
Issue Number	Issue 2, Mar (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/cje/29/2
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/cje.2020.01.009
Journal	Chinese Journal of Electronics
Publisher Date	2020-03-01
Access Restriction	Open
Rights Holder	© Chinese Institute of Electronics
Subject Keyword	Asian Women CNN Model Compacted Presentation Computer Vision Computer Vision And Image Processing Technique Convolutional Neural Nets Data Augmentation Deep Convolution Neural Network Face Recognition Facial Beauty Prediction Feature Extraction GoogleNet Image Classification Image Recognition Large Scale Database Learning in AI Machine Vision Max-feature-max Activation Layer Neural Computing Technique Objection Detection Public Database Small-scale Database Small-scale Parameter Visual Database
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Electrical and Electronic Engineering

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