A Subspace Incremental Learning Method for Face Retrieval In Feature-Length Films

This paper presents a novel method for face retrieval in feature-length films.At first,the AdaBoost is used to detect human faces in the video,then the detected faces are normalized and projected to the incremental eigenspace to obtain their vector representation.After that,the one-class support vector machine is trained for classification and the optimal classification plane is dynamically adjusted to account for different actors in the film.Experiments on a famous Chinese film “little flower” and an Oscar prize film “Notting hill” show the effectiveness of our proposed method.