Age estimation using support vector machine

Abstract EN

Recently there has been an urgent need to identify the ages from personal images.

this paper utilized machine learning technique to intelligent age estimation from facial images by support vector machine (svm) with linear discriminate analysis (lda) using fg_net dataset.

the proposed work consists of three phases: the first phase is image preprocessing include four stages: grayscale image stage, histogram equalization stage, face detection stage has been carried out using viola jones algorithm, it comprises for four steps namely: haar like feature, integral image, adaboost training, and cascading classifier, the last stage of image preprocessing phase is cropping and resize stage.

the second phase is data mining include two stages: feature extraction stage using linear discriminate analysis and machine learning stage using support vector machine.

the last phase is age estimation and evaluation.

the fg-net dataset is used which divided into seven classes.

after extracting the features from the seven classes.

it has been found that some classes have the same number of features.

hence, the seven classes were combined into three classes depending on the number of features it contain, in order to become increased accuracy and reduce the execution time.

the experimental results display that the proposed system can grant high accuracy.

the practical evaluation of the proposed system gives accuracy about 84%.