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.
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Kim, S., Mallipeddi, R., Lee, M. (2012). Incremental Face Recognition: Hybrid Approach Using Short-Term Memory and Long-Term Memory. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7663. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34475-6_24
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DOI: https://doi.org/10.1007/978-3-642-34475-6_24
Publisher Name: Springer, Berlin, Heidelberg
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