The Integrated Usage of LBP and HOG Transformations and Machine Learning Algorithms for Age Range Prediction from Facial Images

Age prediction is an active study field that can be used in many computer vision problems due to its importance and effectiveness. In this paper, we present extensive experiments and provide an efficient and accurate approach for age range prediction of people from facial images. First, we apply image resizing to unify all images’ size, and Histogram Equalization technique to reduce the illumination effects on all facial images taken from FG-NET and UTD aging databases. Second, Histogram of Oriented Gradient (HOG) and Local Binary Pattern (LBP) are used to extract the features of these images, and then we combined both HOG and LBP features in order to attain better prediction. Finally, Support Vector Machine (SVM) and k-Nearest Neighbour (k-NN) are used for the classification processes. In addition, k-fold, Leave-One-Out (LOO) and Confusion Matrix (CM) are used to evaluate the performance of proposed methods. The extensive and intensified experiments show that combining HOG and LBP features improved the age range predicting performance up to 99.87%

Robust approaches for face recognition

This thesis gave answers to a number of important questions regarding face classification. Via this research, new methods were introduced to represent four facial attributes (three of them related to the demographic information of the human face: gender, age and race) and the fourth one related to facial expression. It stated that, discriminative facial features regarding to demographic information (gender, age and race) and expression information can be obtained by applying texture analysis techniques to the polar raster sampled images. In addition, it is found that, multi-label classification (MLC) is more suitable in the real world as a human face can be associated with multiple labels