Face recognition by incremental learning

One of the important features for human machine interaction is its ability to recognize human faces. This paper presents a novel architecture suitable for real time robotic face recognition by learning a person's face incrementally, where the Gabor features at respective feature locations of a face are used to derive a similarity measurement. A face tracking followed by a clustering technique is used to learn a person's face appearance variance when the system interacts with the person. The recognition by learning proposed in this paper is similar to the partial memory incremental learning method, where we proposed a novel approach to the learning and updating process. Experiment shows significant improvement in the face recognition performance after learning over the time and with more interaction between a person and the system.