Age Group Estimation on Single Face Image Using Blocking ULBP and SVM

摘要：

　　Since age implies essential individual information for human beings,age estimation has more and more applications in intelligent human–computer interactions and personalized recommendation in SNS,etc.However,precise age estimation based on single image is difficult due to diverse appearances among people,and irregular quality of sample acquisition.Based on general knowledge that wrinkles increase with age,Uniform Local Binary Patterns(ULBP)is always an effective texture descriptor,but it loses relative location information.In this paper,an age group estimation algorithm is proposed,where after efficient preprocessing,blocking ULBP is used to gain facial textures and a trained multi-class SVM is applied to fulfill age classification.The ages of subjects are divided into five groups: children(0–6),juveniles(7–18),youth(18–40),middle-aged(40-65),and old people(≥66).Experiments are implemented on FG-NET and Morph Aging Database and the estimation accuracy achieves 81.27%.

关键词： Age group estimation ULBP PCA SVM

作者: Liang Hu Zheyuan Li Hong Liu

作者单位: School of Science,Minzu University of China,Beijing 100081,China Engineering Lab on Intelligent Perception for Internet of Things(ELIP),Shenzhen Graduate School Peki

会议类型: 国际会议

会议名称: The 2015 Chinese Intelligent Automation Conference(2015中国智能自动化会议)

会议地点: 福州

会议语种:英文

页码: 431-438

在线出版日期: 2015-05-08（万方平台首次上网日期，不代表论文的发表时间）