Computer Science ›› 2018, Vol. 45 ›› Issue (8): 41-49.doi: 10.11896/j.issn.1002-137X.2018.08.008

• ChinaMM 2017 • Previous Articles     Next Articles

Research on Regional Age Estimation Model

SUN Jin-guang, RONG Wen-zhao   

  1. School of Electronic and Information Engineering,Liaoning Technical University,Huludao,Liaoning 125105,China
  • Received:2017-10-25 Online:2018-08-29 Published:2018-08-29

Abstract: With the further research on age feature extraction and age feature classification pattern,in order to make further efforts to meet the application demand of human-computer interaction system based on age information in real life,constructing an effective machine learning algorithm has become a research focus in age estimation technology of face image.Firstly,this paper analyzed the rule of multiple regional features changing with age,and divided the face into prefrontal region,eye region,central region and integrated region.Then,it constructed features extraction model of deep convolutional neural network models separately to extract age features of each region.Thirdly,taking Morph face database as the sample set,this paper divided it into 6 stages aged 10~19,20~29,30~39,40~49,50~59,and 60 years or older to train and test age feature extraction network model in multiple regions.Finally,according to the accuracy of age feature classification model,this paper defined the region-based dynamic weight age estimation model.The experiment shows that the accuracy of age estimation on Morph face database is 72.6%,and the age classification category has been raised from 4 to 6.

Key words: Age characteristics, Age estimation, Deep learning, Dynamic weights, Morph age database

CLC Number: 

  • TP391
[1]KWON Y H.Age classification from facial images[C]∥1994 IEEE Computer Society Conference on Computer Vision and Pattern Recognition,1994(CVPR’94).IEEE,1994:762-767.
[2]HORNG W B.Classification of age groups based on facial features[J].淡江理工学刊,2001,4(3):183-192.
[3]DEHSHIBI M M,BASTANFARD A.A new algorithm for age recognition from facial images[J].Signal Processing,2010,90(8):2431-2444.
[4]HAYASHI J,YASUMOTO M,ITO H,et al.Age and gender estimation based on wrinkle texture and color of facial images[C]∥16th International Conference on Pattern Recognition,2002.IEEE,2002:405-408.
[5]NAKANO M,YASUKATA F,FUKUMI M.Age classification from face images focusing on edge information[C]∥Knowledge-based Intelligent Information and Engineering Systems.Springer Berlin/Heidelberg,2004:898-904.
[6]TXIA J D,HUANG C L.Age estimation using AAM and local facial features[C]∥Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing,2009(IIH-MSP’09).IEEE,2009:885-888.
[7]GUO G,MU G,FU Y,et al.Human age estimation using bio-inspired features[C]∥IEEE Conference on Computer Vision and Pattern Recognition,2009(CVPR 2009).IEEE,2009:112-119.
[8]ZHANG Y,ZHOU Z H.A new age estimation method based on ensemble learning[J].Acta Automatica Sinica,2008,34(8):997-1000.(in Chinese)张宇,周志华.基于集成的年龄估计方法[J].自动化学报,2008,34(8):997-1000.
[9]YU Q,DU J X.Age estimation of facial images based on an improved non-negative matrix factorization algorithms[J].Journal of Imageand Graphics,2008,13(10):1865-1868.(in Chinese)余庆,杜吉祥.基于一种改进NMF算法的人脸年龄估计方法[J].中国图象图形学报,2008,13(10):1865-1868.
[10]DU J X,YU Q,ZHAI C M.Age estimation of facial images based on non-negative matrix factorization with sparseness constraints[J].Journal of Shandong University (Natural Science),2010,45(7):65-69.(in Chinese)杜吉祥,余庆,翟传敏.基于稀疏性约束非负矩阵分解的人脸年龄估计方法[J].山东大学学报(理学版),2010,45(7):65-69.
[11]GENG X,ZHOU Z H,SMITH-MILES K.Automatic age estimation based on facial aging patterns[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2007,29(12):2234-2240.
[12]LEVI G,HASSNER T.Age and gender classification using convolutional neural networks[C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops.2015:34-42.
[13]YI D,LEI Z,LI S Z.Age estimation by multi-scale convolutional network[C]∥Asian Conference on Computer Vision.Springer International Publishing,2014:144-158.
[14]ROTHE R,TIMOFTE R,VAN GOOL L.Dex:Deep expecta-tion of apparent age from a single image[C]∥Procee-dings of the IEEE International Conference on Computer Vision Workshops.2015:10-15.
[15]LIU X,LI S,KAN M,et al.Agenet:Deeply learned regressor and classifier for robust apparent age estimation[C]∥Procee-dings of the IEEE International Conference on Computer Vision Workshops.2015:16-24.
[16]ANTIPOV G,BACCOUCHE M,BERRANI S A,et al.Appa-rent age estimation from face images combining general and children-specialized deep learning models[C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops.2016:96-104.
[17]KRIZHEVSKY A,SUTSKEVER I,HINTON G E.Imagenetclassification with deep convolutional neural networks[C]∥Advances in Neural Information Processing Systems.2012:1097-1105.
[18]LEMPERLE G,HOLMES R E,LEMPERLE S S M.A Classification of Facial Wri[J].Plastic and Reconstructive Surgery,2001,108(6):1735-1750.
[19]LI Y L,ZHENG L B,YU K L,et al.Variation of head and facial morphological characteristics with increased age of Han in Southern China[J].Chinese Science Bulletin,2013,58(4/5):517-524.(in Chinese)李咏兰,郑连斌,宇克莉,等.南方汉族人头面部形态特征的年龄变化[J].科学通报,2013,58(4):336-343.
[20]SONG X,ZHANG X H,YU K L,et al.Age variations of morphological traits of head and face in Qiangnationality in Sichuan [J].Journal of Tianjin Normal University (Natural Science Edition),2016,36(2):69-74.(in Chinese)宋雪,张兴华,宇克莉,等.四川羌族头面部特征的年龄变化[J].天津师范大学学报 (自然科学版),2016,36(2):69-74.
[21]LI Y L,LU S H,ZHENG L B,et al.Age variation of head and facial morphology in Han nationality in Jiangxi province [J].Acta an Thropologica Sinica,2012,31(2):193-201.(in Chinese)李咏兰,陆舜华,郑连斌,等.江西汉族人头面部形态特征的年龄变化[J].人类学学报,2012,31(2):193-201.
[22]李咏兰,郑连斌,宇克莉,等.中国北方汉族人头面部形态特征的年龄变化[C]∥中国解剖学会2015年年会论文文摘汇编.2015.
[23]ZHENG L B,ZHANG X H,HU Y,et al.Variation of Morphological Traits in Head-facial Values of Han in Qionglai of Sichuan Province[J].Journal of Sun Yat-sen University (Medical Sciences),2011,32(6):729-734.(in Chinese)郑连斌,张兴华,胡莹,等.四川邛崃汉族头面部形态特征的年龄变化[J].中山大学学报(医学科学版),2011,32(6):729-734.
[24]RICANEK K,TESAFAYE T.Morph:A longitudinal image database of normal adult age-progression[C]∥7th International Conference on Automatic Face and Gesture Recognition,2006(FGR 2006).IEEE,2006:341-345.
[25]WANG X M,LIANG L Y,WANG Z L,et al.Age estimation by facial image:a survey[J].Journal of Image and Graphics,2012,17(6):603-618.(in Chinese)王先梅,梁玲燕,王志良,等.人脸图像的年龄估计技术研究[J].中国图象图形学报,2012,17(6):603-618.
[1] XU Yong-xin, ZHAO Jun-feng, WANG Ya-sha, XIE Bing, YANG Kai. Temporal Knowledge Graph Representation Learning [J]. Computer Science, 2022, 49(9): 162-171.
[2] RAO Zhi-shuang, JIA Zhen, ZHANG Fan, LI Tian-rui. Key-Value Relational Memory Networks for Question Answering over Knowledge Graph [J]. Computer Science, 2022, 49(9): 202-207.
[3] TANG Ling-tao, WANG Di, ZHANG Lu-fei, LIU Sheng-yun. Federated Learning Scheme Based on Secure Multi-party Computation and Differential Privacy [J]. Computer Science, 2022, 49(9): 297-305.
[4] SUN Qi, JI Gen-lin, ZHANG Jie. Non-local Attention Based Generative Adversarial Network for Video Abnormal Event Detection [J]. Computer Science, 2022, 49(8): 172-177.
[5] WANG Jian, PENG Yu-qi, ZHAO Yu-fei, YANG Jian. Survey of Social Network Public Opinion Information Extraction Based on Deep Learning [J]. Computer Science, 2022, 49(8): 279-293.
[6] HAO Zhi-rong, CHEN Long, HUANG Jia-cheng. Class Discriminative Universal Adversarial Attack for Text Classification [J]. Computer Science, 2022, 49(8): 323-329.
[7] JIANG Meng-han, LI Shao-mei, ZHENG Hong-hao, ZHANG Jian-peng. Rumor Detection Model Based on Improved Position Embedding [J]. Computer Science, 2022, 49(8): 330-335.
[8] HOU Yu-tao, ABULIZI Abudukelimu, ABUDUKELIMU Halidanmu. Advances in Chinese Pre-training Models [J]. Computer Science, 2022, 49(7): 148-163.
[9] ZHOU Hui, SHI Hao-chen, TU Yao-feng, HUANG Sheng-jun. Robust Deep Neural Network Learning Based on Active Sampling [J]. Computer Science, 2022, 49(7): 164-169.
[10] SU Dan-ning, CAO Gui-tao, WANG Yan-nan, WANG Hong, REN He. Survey of Deep Learning for Radar Emitter Identification Based on Small Sample [J]. Computer Science, 2022, 49(7): 226-235.
[11] HU Yan-yu, ZHAO Long, DONG Xiang-jun. Two-stage Deep Feature Selection Extraction Algorithm for Cancer Classification [J]. Computer Science, 2022, 49(7): 73-78.
[12] CHENG Cheng, JIANG Ai-lian. Real-time Semantic Segmentation Method Based on Multi-path Feature Extraction [J]. Computer Science, 2022, 49(7): 120-126.
[13] LIU Wei-ye, LU Hui-min, LI Yu-peng, MA Ning. Survey on Finger Vein Recognition Research [J]. Computer Science, 2022, 49(6A): 1-11.
[14] SUN Fu-quan, CUI Zhi-qing, ZOU Peng, ZHANG Kun. Brain Tumor Segmentation Algorithm Based on Multi-scale Features [J]. Computer Science, 2022, 49(6A): 12-16.
[15] KANG Yan, XU Yu-long, KOU Yong-qi, XIE Si-yu, YANG Xue-kun, LI Hao. Drug-Drug Interaction Prediction Based on Transformer and LSTM [J]. Computer Science, 2022, 49(6A): 17-21.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!