Title :
Incremental face recognition using rehearsal and recall processes
Author :
Sangwook Kim ; Mallipeddi, R. ; Minho Lee
Author_Institution :
Sch. of Electron. Eng., Kyungpook Nat. Univ., Taegu, South Korea
Abstract :
Most of the machine learning algorithms particularly suffer from the plasticity-stability dilemma. In this paper, we propose a model that adopts two types of memories i.e. short-term memory (STM) and long-term memory (LTM), which share their information through control processes called rehearsal and recall to alleviate the dilemma. In addition, the proposed model tries to integrate the advantages of generative and discriminative classifiers by employing them in STM and LTM respectively. Experimental results show the importance of rehearsal and recall process in improving the performance of the algorithm.
Keywords :
face recognition; image classification; learning (artificial intelligence); LTM learning process; STM learning process; discriminative classifiers; generative classifiers; incremental face recognition; long-term memory; recall process; rehearsal process; short-term memory; Data models; Face recognition; Feature extraction; Principal component analysis; Robustness; Support vector machines; Training;
Conference_Titel :
Neural Networks (IJCNN), 2014 International Joint Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4799-6627-1
DOI :
10.1109/IJCNN.2014.6889902