Incremental face recognition: hybrid approach using short-term memory and long-term memory

Authors:
Sangwook Kim;Rammohan Mallipeddi;Minho Lee
Affiliations:
School of Electrical Engineering and Computer Science, Kyungpook National University, Taegu, South Korea;School of Electrical Engineering and Computer Science, Kyungpook National University, Taegu, South Korea;School of Electrical Engineering and Computer Science, Kyungpook National University, Taegu, South Korea
Venue:
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part I
Year:
2012

Citing 9
Cited 0

The nature of statistical learning theory

The nature of statistical learning theory
Generative versus Discriminative Methods for Object Recognition

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Combining Generative and Discriminative Learning for Face Recognition

DICTA '05 Proceedings of the Digital Image Computing on Techniques and Applications
Estimating the Support of a High-Dimensional Distribution

Neural Computation
Robust Face Recognition via Sparse Representation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Evolving logic networks with real-valued inputs for fast incremental learning

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
Face recognition based on multi-class SVM

CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Is face recognition really a Compressive Sensing problem?

CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
A novel incremental principal component analysis and its application for face recognition

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Quantified Score

Hi-index	0.00

Visualization

Abstract

To overcome the plasticity-stability dilemma in incremental face recognition algorithms, we propose a model that employs --- short-term memory (STM) and long-term memory (LTM) based on the Atkinson theory. During the incremental learning the STM can learn the incoming data quickly but due to the limited capacity tends to forget the previously learnt data while trying to learn the new incoming data. Conversely, LTM takes more time to learn the new data but can incorporate the new incoming data effectively while maintaining the previously learnt data. In this paper, we try to improve the learning capability of the STM by using the information present in the LTM by a recall process. To show the effectiveness of the recall process, we evaluated the performance of the STM with and without the recall operation. Experimental results show the successful face recognition performance of the proposed method and the importance of the recall process.