@Article{JICS-14-217,
author = {Yulin Li},
title = {Face age and gender recognition based on improved VGGNet algorithm},
journal = {Journal of Information and Computing Science},
year = {2024},
volume = {14},
number = {3},
pages = {217--226},
abstract = {School of Mathematics and Statistics, Nanjing University of Information Science & Technology,  
Nanjing, 210044, China 
(Received February 20 2019, accepted June 24 2019) 
 Recognition  of  age  and  gender  based  on  face  image  is  one  of  the  hotspots  of  current  artificial 
intelligence  research.  In  this  paper,  an  improved  VGG+SENet  algorithm  is  proposed  to  simplify  the 
identification of age  and gender  algorithm by simplifying  VGGNet model, improving the loss  function  and 
embedding the SENet module. Compared with other models, the improved network structure and loss function 
model proposed in this paper can quickly and accurately obtain output recognition results. Experimental results 
on  multiple  benchmark  face  datasets  show  that  the  proposed  improved  VGG+SENet  algorithm  has  higher 
recognition accuracy than other related models based on deep learning. 
},
issn = {1746-7659},
doi = {https://doi.org/},
url = {http://global-sci.org/intro/article_detail/jics/22416.html}
}