Abstract

Abstract: In recent trends estimating human ageautomatically from face images have lot of potential in real worldapplications, such as vending machine, security system/network access control,human computer interaction, multimedia communication, video surveillance,customer profiling, demographic statistics collection etc. Securityapplications have atmost importance in this area.The biometric features of each human being areunique.  Age estimation is determines aperson’s age or age group using facial images. A database of facial images istrained to extract features using algorithms such local binary patterns [LBP],active shape models [ASM], histogram of oriented gradients [HOG], SupportVector Machine[SVM]. Age estimation can be done using 3 age groups: child,adult, senior. Age estimation can be used as part of a facerecognition process. This paper presents a comprehensive comparison ofstate-of-the-art research techniques. We have divided the classificationprocess into three stages and have presented a categorical review of existingliteratures. Their analysis has been presented.

Keywords:Age Estimation, Pre-processing,  FeatureExtraction, Classification, Comprehensive Review.