IEEE Intelligent Systems
Computer Vision and Image Understanding - Special issue on Face recognition
Spatial Interest Pixels (SIPs): Useful Low-Level Features of Visual Media Data
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Face recognition based on polar frequency features
ACM Transactions on Applied Perception (TAP)
Face recognition using point symmetry distance-based RBF network
Applied Soft Computing
Continuous Verification Using Multimodal Biometrics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Performance of similarity measures based on histograms of local image feature vectors
Pattern Recognition Letters
A real-time, embedded face-annotation system
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Gabor Filter Based Efficient Thermal and Visual Face Recognition Using Fusion Architectures
Proceedings of the 2006 conference on Advances in Intelligent IT: Active Media Technology 2006
A compilation framework for power and energy management on mobile computers
LCPC'01 Proceedings of the 14th international conference on Languages and compilers for parallel computing
Continuous verification using multimodal biometrics
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
Design of face recognition door manager system based on DSP
EUC'05 Proceedings of the 2005 international conference on Embedded and Ubiquitous Computing
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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.