Active Shape Model with Multiple Classifiers for Age Prediction - Susilawati Mohamad, Fatma; Iqtait, Musab (International Journal of Engineering and Technology, 2018)
Automatic age prediction from facial images has received much attention. This is due to its various applications in security control, law enforcement, and human computer interaction. In spite of its developments, age prediction becomes more challenging. This is because the facial age procedure is specified not only by internal factors like genetic factors and external factors like lifestyle and environ-mental factors. In this paper, an enhanced age prediction algorithm using Active Shape Model (ASM) with six classifiers is suggested. 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) are adopted in order to enhance the accuracy of age prediction. In this work, traits of the facial images are extracted via ASM as the trait vector. The classifiers are utilized and compared to predict the age. The experimental results indicated that CCA has the highest accuracy while KNN has the lowest.Â