Online person identification and new person discovery using appearance features

Abstract

Person identification is an important but still challenging problem in video surveillance. This work designs a completely automatic appearance-based person identification system, which has the ability to achieve new person discovery and classification. The proposed system consists of three modules: background and silhouette separation; feature extraction and selection; and online person identification. The Self-Adaptive Kernel Machine (SAKM) algorithm is used to differentiate existing persons who can be classified from new persons who have to be learnt and added. A new video database with 22 persons is created in real-life environments. The experimental results show that the proposed system achieves satisfying recognition rates of over 90% on person classification with novelty identification.