Active Appearance Model for Age Prediction: A Comparison

Abstract

Individual age gives key demographic data. It is viewed as a paramount delicate biometric characteristic for individual identification, contrasted with other pattern recognition issues. Age estimation is a complex issue particularly in relation to facial pictures with different ages, since the aging procedure varies extraordinarily across different age groups. In this work, we investigate deep learning techniques for age prediction based on Active Appearance Models (AAM) and six classifiers: Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Support Vector Regression (SVR), Canonical Correlation Analysis (CCA), Linear Discriminant Analysis (LDA) and Projection Twin Support Vector Machine (PTSVM) to improve the precision of age prediction based on the present methods. In this algorithm, we extracted the traits of the facial images as traits vectors using AAM model, and the classifiers are utilized to predict the age. We were able to recognize that the accuracy of CCA algorithm is the best, the intermediate is SVR and the KNN algorithm is the lowest.