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Age Estimation Using Appearance Images for Human-Robot Interaction

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There are many modern applications require the function of age estimation such as security control and surveillance monitoring, health care system and so on. 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 and preprocessed. Our image database is from FG-NET and MORPH databases so that we have high degree of complexity in training dataset. 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 F-measure based weighting policy. 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. For the purpose of human-machine interaction, we design a simple user interface to perform online age-group classification. The system can be applied on any computer or robot as long as it has a camera sensor.