Face age and gender recognition based on improved VGGNet algorithm

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Volume 14, Issue 3

Yulin Li

J. Info. Comput. Sci. , 14 (2019), pp. 217-226.

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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.

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@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 = {2019}, 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 = {3080-180X}, doi = {https://doi.org/}, url = {http://global-sci.org/intro/article_detail/jics/22416.html} }

TY - JOUR T1 - Face age and gender recognition based on improved VGGNet algorithm AU - Yulin Li JO - Journal of Information and Computing Science VL - 3 SP - 217 EP - 226 PY - 2019 DA - 2019/09 SN - 14 DO - http://doi.org/ UR - https://global-sci.org/intro/article_detail/jics/22416.html KW - AB - 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.

Yulin Li. (2019). Face age and gender recognition based on improved VGGNet algorithm. Journal of Information and Computing Science. 14 (3). 217-226. doi:

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