This paper proposes “seamless customer identification” (SCI), a means to identify physically present customers without any effort on customers’ part beyond a one-time opt-in. With SCI, customers need not present cards or operate smartphones to convey their identities. So, stores can provide personalized shopping experiences at any time, not just at check-out. SCI uses two complementary technologies: device detection and face recognition. Device detection identifies customers by detecting their phones through low-power wireless
discovery, in our case using Bluetooth Low Energy (BLE). Face recognition recognizes customers by matching pictures captured during registration with images captured by a store camera. Device detection makes face recognition feasible by limiting the number of potential customers, while face recognition provides directional information that device detection lacks.
Together, these technologies provide an ordering of likely candidates to a store employee, who makes the final determination of identity. We have designed and built SCI, and demonstrated its usefulness in an application called Zero-Effort Payments (ZEP). ZEP uses SCI to let customers effortlessly make small purchases at a coffee stand. We conducted two real-world
deployments of ZEP on actual customers: a two-day deployment during a technology fair and a four-month deployment in our building. Across both deployments, 274 customers made 705 purchases using ZEP. Through these deployments and other experiments, we demonstrate how
our techniques make seamless customer identification feasible and practical.