Feature extraction for facial age estimation: A survey

Human age estimation is very interesting challenge today also. The prediction of age based on different techniques and by human, itself is very similar now. The major challenges for machine based age estimation are still alive. Challenges are uncontrolled environment, different light settings, variant posing and expression, ageing effects and life style. Human age estimation started with craniofacial growth has now reached to the condition where challenges are changing into opportunities for developing of applications. Applications include Biometrics, forensic investigation during crime, Relationship management over e-commerce, security control, Monitoring and surveillance, entertainment, cosmetology. Computer-based age estimation via facial images includes stages such as pre-processing, feature extraction, feature reduction and lastly classification or regression. Pre-processing provides invariability against different light settings and environment. Feature extraction deals with extraction of features like facial landmarks, shape and texture for further analysis. Feature reduction is considered for faster analysis of feature vectors. Lastly, regression, classification or combination is used for estimation of the age.