Age estimation by LS-SVM regression on facial images

Determining the age of a person just by using an image of his/her face is a research topic in Computer Vision that is being extensively worked on. In contrast to say expression analysis, age determination is dependent on a number of factors. To construe the real age of a person is an esoteric task. The changes that appear on a face are not only due to aging, but also a number of factors like stress, appropriate rest etc. In this paper an approach has been developed to determine true age of a person by making use of some existing algorithms and combining them for maximum efficiency. The image is represented using an Active Appearance Model (AAM). The AAM uses geometrical ratio of the local face features along with wrinkle analysis. Next, to enhance the feature selection, Principle Component Analysis (PCA) is done. For the learning process a Support Vector Machine is used. Relationships in the image are obtained by making use of binarized statistical image features (BSIF) and the patterns are stored in Local Binary Pattern Histograms (LBPH). This histogram acts as input for the learning unit. The SVM is made to learn the patterns by studying the LBPH. Finally after the learning phase, when a new image is taken, a Least Square-Support Vector Machine Regression model (LS-SVM) is used to predict the final age of the person in the image.