Age Estimation Using Gender Information

Estimating age from a facial image is a intriguing mid exigent task. Aging changes both shape as well as texture and it is an irreversible, uncontrollable and personalized. The way of aging in male is different from female and hence the accuracy of age estimation process can be improved if it is preceded by gender classification. The work proposed in this paper takes care of this by using gender information for categorizing age range of the given face image. Appearance parameters (AAM), containing shape and texture variations is used for gender classification which is analyzed with two well known classifiers Neural Networks and Support Vector Machines (SVM). Gender classified appearance parameters are fed into male or female age estimator. Age estimation is then performed using Neural networks which classifies age range of the given face image. Experimental results on FG-NET age database demonstrate the effectiveness of the framework and validates that performance is better than existing approaches. The results also shows that appearance parameter from AAM increases the performance of the gender classification.