Evaluation of face recognition techniques for application to facebook

This paper evaluates face recognition applied to the real-world application of Facebook. Because papers usually present results in terms of accuracy on constrained face datasets, it is difficult to assess how they would work on natural data in a real-world application. We present a method to automatically gather and extract face images from Facebook, resulting in over 60,000 faces datasets, we evaluate a variety of well-known face recognition algorithms (PCA, LDA, ICA, SVMs) against holistic performance metrics of accuracy, speed, memory usage, and storage size. SVMs perform best with ~65% accuracy, but lower accuracy algorithms such as IPCA are orders of magnitude more efficient in memory consumption and speed, yielding a more feasible system.