High-Performance Memory-based Face Recognition for Visitor Identification

We show that a simple, memory-based technique for view-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. This paper further demonstrates that our algorithm has desirable asymptotic computational and storage behavior, and is ideal for incremental training. Our system is incorporated into an automated visitor identification system that has been operating successfully in an outdoor environment for several months.