计算机科学 ›› 2018, Vol. 45 ›› Issue (8): 41-49.doi: 10.11896/j.issn.1002-137X.2018.08.008

• 2017 中国多媒体大会 • 上一篇    下一篇

基于区域的年龄估计模型研究

孙劲光, 荣文钊   

  1. 辽宁工程技术大学电子与信息工程学院 辽宁 葫芦岛125105
  • 收稿日期:2017-10-25 出版日期:2018-08-29 发布日期:2018-08-29
  • 作者简介:孙劲光(1962-),女,博士,教授,CCF高级会员,主要研究方向为计算机图像处理、计算机图形学、知识工程; 荣文钊(1990-),男,硕士生,主要研究方向为计算机图像处理,E-mail:mrdlzhao@aliyun.com(通信作者)。
  • 基金资助:
    本文受国家自然科学基金青年基金:地震勘探大数据的高精度处理技术研究(61602226)资助。

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

摘要: 随着年龄特征提取和年龄特征分类模式研究的不断深入,为了进一步满足基于年龄信息的人机交互系统在现实生活中的应用需求,构建有效的机器学习算法已成为人脸图像年龄估计技术的研究热点之一。首先,通过分析人脸图像的多个区域特征随年龄变化的规律,将面部分为前额区域、眼部区域、面中部区域及人脸整体区域,并分别构建深度卷积神经网络特征提取模型,实现每个区域年龄的特征提取;其次,以 Morph人脸库为样本集,将其划分为10~19岁、20~29岁、30~39岁、40~49岁、50~59岁、60岁以上6个年龄段,完成多区域年龄特征提取网络模型的训练及测试;最后,依据多区域网络年龄特征分类的准确率,确定基于区域的动态权值年龄估计模型。实验表明:所提模型在Morph人脸库中的年龄估计准确率达到72.6%,也将该人脸库的年龄分类类别由4个提升到6个。

关键词: Morph年龄库, 动态权值, 年龄估计, 年龄特征, 深度学习

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

中图分类号: 

  • 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] 徐涌鑫, 赵俊峰, 王亚沙, 谢冰, 杨恺.
时序知识图谱表示学习
Temporal Knowledge Graph Representation Learning
计算机科学, 2022, 49(9): 162-171. https://doi.org/10.11896/jsjkx.220500204
[2] 饶志双, 贾真, 张凡, 李天瑞.
基于Key-Value关联记忆网络的知识图谱问答方法
Key-Value Relational Memory Networks for Question Answering over Knowledge Graph
计算机科学, 2022, 49(9): 202-207. https://doi.org/10.11896/jsjkx.220300277
[3] 汤凌韬, 王迪, 张鲁飞, 刘盛云.
基于安全多方计算和差分隐私的联邦学习方案
Federated Learning Scheme Based on Secure Multi-party Computation and Differential Privacy
计算机科学, 2022, 49(9): 297-305. https://doi.org/10.11896/jsjkx.210800108
[4] 王剑, 彭雨琦, 赵宇斐, 杨健.
基于深度学习的社交网络舆情信息抽取方法综述
Survey of Social Network Public Opinion Information Extraction Based on Deep Learning
计算机科学, 2022, 49(8): 279-293. https://doi.org/10.11896/jsjkx.220300099
[5] 郝志荣, 陈龙, 黄嘉成.
面向文本分类的类别区分式通用对抗攻击方法
Class Discriminative Universal Adversarial Attack for Text Classification
计算机科学, 2022, 49(8): 323-329. https://doi.org/10.11896/jsjkx.220200077
[6] 姜梦函, 李邵梅, 郑洪浩, 张建朋.
基于改进位置编码的谣言检测模型
Rumor Detection Model Based on Improved Position Embedding
计算机科学, 2022, 49(8): 330-335. https://doi.org/10.11896/jsjkx.210600046
[7] 孙奇, 吉根林, 张杰.
基于非局部注意力生成对抗网络的视频异常事件检测方法
Non-local Attention Based Generative Adversarial Network for Video Abnormal Event Detection
计算机科学, 2022, 49(8): 172-177. https://doi.org/10.11896/jsjkx.210600061
[8] 胡艳羽, 赵龙, 董祥军.
一种用于癌症分类的两阶段深度特征选择提取算法
Two-stage Deep Feature Selection Extraction Algorithm for Cancer Classification
计算机科学, 2022, 49(7): 73-78. https://doi.org/10.11896/jsjkx.210500092
[9] 程成, 降爱莲.
基于多路径特征提取的实时语义分割方法
Real-time Semantic Segmentation Method Based on Multi-path Feature Extraction
计算机科学, 2022, 49(7): 120-126. https://doi.org/10.11896/jsjkx.210500157
[10] 侯钰涛, 阿布都克力木·阿布力孜, 哈里旦木·阿布都克里木.
中文预训练模型研究进展
Advances in Chinese Pre-training Models
计算机科学, 2022, 49(7): 148-163. https://doi.org/10.11896/jsjkx.211200018
[11] 周慧, 施皓晨, 屠要峰, 黄圣君.
基于主动采样的深度鲁棒神经网络学习
Robust Deep Neural Network Learning Based on Active Sampling
计算机科学, 2022, 49(7): 164-169. https://doi.org/10.11896/jsjkx.210600044
[12] 苏丹宁, 曹桂涛, 王燕楠, 王宏, 任赫.
小样本雷达辐射源识别的深度学习方法综述
Survey of Deep Learning for Radar Emitter Identification Based on Small Sample
计算机科学, 2022, 49(7): 226-235. https://doi.org/10.11896/jsjkx.210600138
[13] 祝文韬, 兰先超, 罗唤霖, 岳彬, 汪洋.
改进Faster R-CNN的光学遥感飞机目标检测
Remote Sensing Aircraft Target Detection Based on Improved Faster R-CNN
计算机科学, 2022, 49(6A): 378-383. https://doi.org/10.11896/jsjkx.210300121
[14] 王建明, 陈响育, 杨自忠, 史晨阳, 张宇航, 钱正坤.
不同数据增强方法对模型识别精度的影响
Influence of Different Data Augmentation Methods on Model Recognition Accuracy
计算机科学, 2022, 49(6A): 418-423. https://doi.org/10.11896/jsjkx.210700210
[15] 毛典辉, 黄晖煜, 赵爽.
符合监管合规性的自动合成新闻检测方法研究
Study on Automatic Synthetic News Detection Method Complying with Regulatory Compliance
计算机科学, 2022, 49(6A): 523-530. https://doi.org/10.11896/jsjkx.210300083
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!