Human age classification using appearance images for human-robot interaction

Abstract

There are many modern applications require the function of age classification. In this study, we propose a method to classify human age using appearance images and apply it to the human-robot interactions. We first confirm that facial features based on craniology are not discriminative under the condition of seven age-groups classification. Next, our system is designed to have two stages. One is image preprocess stage; faces are detected using Haar-like features with Adaboost algorithm. Our image database is from FG-NET and MORPH databases so that we have high degree of complexity and difficulty in recognition. Then images are trained by support vector machines (SVM). To have higher recognition rate, we train RBF (radial basis function) and linear kernel models at the same time, and decide the final results by comparing the two models. These improve the accuracy under age of 30 to 49 years old while the linearity is preserved under age of 0 to 29 and above 50 years old. The final age recognition rates achieve 91.5% for female and 96% for male. We also compare the age-group classification results with subjective questionnaires, and it demonstrates that the proposed system has better performance than human's subjective estimation.