Abstract
Although SVM (Support Vector Machine) is a strong classifier that cannot be combined with AdaBoost (Adaptive Boosting) easily, it has been proved to be effective component classifier in AdaBoost. In this paper, we propose AdaBoost multiple feature selection with SVM for gender and age classification based on the above-mentioned theory. The chosen features have been proved to be successful in describing facial image. They include LBP (Local Binary Pattern), LDP (Local Directional Pattern), LTP (Local Ternary Pattern), Gabor filter, and LGBP (Local Gabor Binary Pattern). To speed up the training process and get better performance, we reduce dimensionalities by two methods: PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis). Then, we use SVM as the component classifier in AdaBoost. At each iteration in AdaBoost, we can choose the most suitable feature by evaluating the training error rate. Finally, we can combine these component classifiers into the ultimate recognition model. From the experimental results, AdaBoostSVM with no feature selection can perform as well as SVM. Moreover, AdaBoostSVM with multiple feature selection has the chance to outperform the other classifiers in both gender and age classification.