Memory-based face recognition for visitor identification

Terence Sim, Rahul Sukthankar, Matthew Mullin, and Shumeet Baluja
Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition, March, 2000, pp. 214 - 220.


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Abstract
We show that a simple, memory-based technique for appearance-based face recognition, motivated by the real-world task of visitor identification, can outperform more sophisticated algorithms that use Principal Components Analysis (PCA) and neural networks. This technique is closely related to correlation templates; however, we show that the use of novel similarity measures greatly improves performance. We also show that augmenting the memory base with additional, synthetic face images results in further improvements in performance. Results of extensive empirical testing on two standard face recognition datasets are presented, and direct comparisons with published work show that our algorithm achieves comparable (or superior) results. Our system is incorporated into an automated visitor identification system that has been operating successfully in an outdoor environment since January 1999.

Keywords
face recognition,

Notes
Associated Center(s) / Consortia: Vision and Autonomous Systems Center
Number of pages: 7

Text Reference
Terence Sim, Rahul Sukthankar, Matthew Mullin, and Shumeet Baluja, "Memory-based face recognition for visitor identification," Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition, March, 2000, pp. 214 - 220.

BibTeX Reference
@inproceedings{Sim_2000_3393,
   author = "Terence Sim and Rahul Sukthankar and Matthew Mullin and Shumeet Baluja",
   title = "Memory-based face recognition for visitor identification",
   booktitle = "Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition",
   pages = "214 - 220",
   month = "March",
   year = "2000",
}