Scaling up Biologically-Inspired Computer Vision: A Case Study in Unconstrained Face Recognition on Facebook

Abstract

Biological visual systems are currently unrivaled by artificial systems in their ability to recognize faces and objects in highly variable and cluttered real-world environments. Biologically-inspired computer vision systems seek to capture key aspects of the computational architecture of the brain, and such approaches have proven successful across a range of standard object and face recognition tasks (e.g. [23, 8, 9, 18]). Here, we explore the effectiveness of these algorithms on a large-scale unconstrained real-world face recognition problem based on images taken from the Face-book social networking website. In particular, we use a family of biologically-inspired models derived from a high-throughput feature search paradigm [19, 15] to tackle a face identification task with up to one hundred individuals (a number that approaches the reasonable size of real-world social networks). We show that these models yield high levels of face-identification performance even when large numbers of individuals are considered; this performance increases steadily as more examples are used, and the models outperform a state-of-the-art commercial face recognition system. Finally, we discuss current limitations and future opportunities associated with datasets such as these, and we argue that careful creation of large sets is an important future direction.