Multi-view video based tracking and audio-visual identification of persons in a human-computer-interaction scenario

User identification and tracking are definitely the basic tasks in any human computer interaction (HCI) scenario. For these tasks we propose a multi-view approach utilizing multi-camera systems and audio processing systems. Face detectors and face recognizers are based on orientation histogram and eigenface techniques, and Mel Frequency Cepstral Coefficients (MFCC) are applied for speaker identification. In order to achieve a robust user identification and localization spatio-temporal classifier fusion methods have been integrated into the overall classifier system, support vector machines (SVM) and k nearest neighbor (kNN) models are used as base classifiers. A general office environment with up to six persons was the test bed for data collection and numerical evaluation.