A real-time personal authentication system based on incremental feature extraction and classification of audiovisual information

We propose a new approach to a real-time personal authentication system based on incrementally updated visual (face) and audio (voice) features of persons. The proposed system consists of real-time face detection, incremental audiovisual feature extraction, and incremental neural classifier model with long-term memory. The face detection part, a biologically motivated face-color preferable selective attention model first localizes face candidate regions in natural scenes, and then the Adaboost-based face detection identifies human faces from the localized face-candidate regions. The mel-frequency cepstral coefficient is used for vocal feature extraction of speakers. Moreover, incremental principal component analysis (IPCA) is used to reduce the dimensions of audiovisual features and to update them incrementally. The features extracted by IPCA is fed to the resource allocating network with long-term memory which learns facial and vocal features incrementally and recognizes faces in real time. Experimental results show that the proposed system can enhance the test performance incrementally without serious forgetting. In addition, a multi-modal (facial and vocal) feature effectively increases the robustness of the personal authentication system in noisy environments.